The use of multi-objective optimization to build classifier ensembles is becoming increasingly popular. This approach optimizes more than one criterion simultaneously and returns a set of solutions. Thus the final solution can be more tailored to the user’s needs. The work proposes the MOONF method using one or two criteria depending on the method’s version. Optimization returns solutions as feature subspaces that are then used to train decision tree models. In this way, the ensemble is created (...) non-randomly, unlike the popular Random Subspace approach (such as the Random Forest classifier). Experiments carried out on many imbalanced datasets compare the proposed methods with state-of-the-art methods and show the advantage of the MOONF method in the multi-objective version. (shrink)

The paper reviews the methodology of multi-objective modeling and optimization used in decision support based on computerized analytical models (as opposed to logical models used in expert systems) that represent expert knowledge in a given field. The essential aspects of this methodology relate to its flexibility: modeling and optimization methods are treated not as goals in themselves but as tools that help a sovereign user (an analyst or a decision maker) to interact with the model, to (...) generate and analyze various decision options, to learn about possible outcomes of these decisions. Although the applications of such methods in negotiation and mediation support is scarce yet, their flexibility increases essentially the chances of such applications. Various aspects of negotiation and mediation methods related to multi-objective optimization and game theory are also reviewed. A possible application of the MCBARG system for supporting negotiation related to the acid rain problem is briefly summarized. (shrink)

Evolutionary multi-objective optimization algorithms have been successfully applied in many real-life problems. EMOAs approximate the set of trade-offs between multiple conflicting objectives, known as the Pareto optimal set. Reference point approaches can alleviate the optimization process by highlighting relevant areas of the Pareto set and support the decision makers to take the more confident evaluation. One important drawback of this approaches is that they require an in-depth knowledge of the problem being solved in order to function (...) correctly. Collective intelligence has been put forward as an alternative to deal with situations like these. This paper extends some well-known EMOAs to incorporate collective preferences and interactive techniques. Similarly, two new preference-based multi-objective optimization performance indicators are introduced in order to analyze the results produced by the proposed algorithms in the comparative experiments carried out. (shrink)

Based on the concept of “passengers self-help hubbing,” we build a flight schedule optimization model where maximizing the number of feasible flight connections, indicating transfer opportunities, as one objective and minimizing total slot displacements as the other objective. At the same time, the “Demand Smoothing Model” is introduced into the flight schedule optimization model to reduce the queuing delays for arrival and departure flights. We take into account all aircraft itineraries, the difficulty level of schedule coordination, (...) and the maximum displacement of any single flight acceptable to airlines when optimizing flight schedule. Given an original schedule, the model produces a feasible modified schedule that obeys the slot limits specified for an airport without canceling any flights, increases transfer opportunities, and improves on-time performance for hub airports while reducing interference with airline scheduling preferences. The model was verified with the operating data of the Urumqi international airport, and the results show that minor adjustments to flight schedules can increase the transfer opportunities of the airport and significantly reduce flight queuing delays. (shrink)

Effective and personalized treatment relies heavily on skin disease categorization. In the stratification of skin disorders, it is crucial to identify the subtypes of illnesses to provide an efficient therapy. To attain this aim, researchers have focused their attention on cluster algorithms for the stratification of skin disorders in recent decades. But, cluster algorithms have real-world drawbacks, including experimental noises, a large number of dimensions, and a poor ability to comprehend. Cluster algorithms, in particular, determine the quality of clusters using (...) a single internal evaluation operation in the majority of cases. A single internal assessment procedure is difficult to design and robust for all datasets, which is a problem. The multi-objective particle swarm obtained high sensitivity in the existing work, but it is not able to anticipate all kinds of classes. An optimized cluster distance parameter for K-means clustering is determined using a hybrid particle swarm and moth flame optimization. Multi-objective is guided by two cluster value indices, including the K-means clustering misclassification rate and neural network classification rate. Hybrid PSO will solve the multi-objective problem to identify the optimal cluster for clustering. On the dermatological dataset from the UCI repository, MATLAB R2020a will be used to evaluate the proposed method. This will be followed by an evaluation of the proposed method’s performance using the cluster evaluation indices. (shrink)

In this paper, a complex non- linear programming problem with the two parts (real and imaginary) is considered. The efficient and proper efficient solutions in terms of optimal solutions of related appropriate scalar optimization problems are characterized. Also, the Kuhn-Tuckers' conditions for efficiency and proper efficiency are derived. This paper is divided into two independently parts: The first provides the relationships between the optimal solutions of a complex single-objective optimization problem and solutions of two related real programming (...) problems. The second part is concerned with the theory of a multi-objective optimization in complex space. (shrink)

This article attempts to study cost minimizing multi-objective fractional solid transportation problem with fuzzy cost coefficients c ˜ i j k r {\tilde{c}}_{ijk}^{r}, fuzzy supply quantities a ˜ i {\tilde{a}}_{i}, fuzzy demands b ˜ j {\tilde{b}}_{j}, and/or fuzzy conveyances e ˜ k {\tilde{e}}_{k}. The fuzzy efficient concept is introduced in which the crisp efficient solution is extended. A necessary and sufficient condition for the solution is established. Fuzzy geometric programming approach is applied to solve the crisp problem by (...) defining membership function so as to obtain the optimal compromise solution of a multi-objective two-stage problem. A linear membership function for the objective function is defined. The stability set of the first kind is defined and determined. A numerical example is given for illustration and to check the validity of the proposed approach. (shrink)

Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization has been proposed. However, external archive maintenance and selection of leaders for (...) designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy to solve the many-objective software package restructuring problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches. (shrink)

In multiple robot systems, the problem of allocation of complex tasks to heterogeneous teams of robots, also known as the multiple robot coalition formation problem, has begun to receive considerable attention. Efforts to address the problem range from heuristics based approaches that search the subspaces of the coalition structure to evolutionary learning approaches. Conventional approaches typically strive to optimize a single objective function such as the number of tasks executed or the time required to execute all tasks, or a (...) weighted function of such objectives. In real world applications, objectives such as minimizing distance traveled and maximizing the number of tasks completed are often conflicting in nature. The coalition formation problem thus naturally lends itself to a multi-objective optimization approach based on evolutionary learning. In this paper, we formulate the problem of mapping coalitions of robots to a set of tasks as a multi-objective optimization problem and propose a variant of non-dominated sorting genetic algorithm to arrive at trade-off solutions. Additionally, we extend the solution to domains where robot resources are non-additive. Simulations demonstrate the efficacy of the proposed approach in generating the set of Pareto-optimal solutions. (shrink)

The machines exhibit an intelligence which is artificial intelligence, and it is the design of intelligent agents. A system is represented by an intelligent agent who perceives its environment and the success rate is maximized by taking the action. The AI research is highly specialized and there are two subfields and each communication fails often. The popular AI approaches include the traditional symbolic AI and computational intelligence. In order to optimize the seismic design of the reinforced concrete pier structure, the (...) particle swarm optimization algorithm and the reaction spectrum analysis method are combined; they establish a regular bridge of the design variable with cross-sectional characteristics and reinforcement ratios, with the target function. The seismic optimization design framework of the pier is transformed into a multi-objective optimization problem. Calculations show that the method can quickly obtain the optimal design parameters that meet multi-objective requirements. The improved PSO main program and the calling push-over program run time are 4.32 and 1347.56 s, respectively; the push-over program running time is 99.68% of the run time of the total program. Optimization of the seismic performance of the rear bridge pier is significantly improved and is more in line with the design method; the design method proposed in this article is more practical. (shrink)

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem. Based on likelihood (...) robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization. (shrink)

With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering (...) on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique. (shrink)

Most of real world problems contain complex and various constraints, and the penalty depending on the degree of violation is often used to handle them. However, two objectives, to reduce the violation and to optimize the primary value, are inherently oppositive. Therefore, using additive penalty method often leads the fatal compromise to a solution with bad primary objective value in return for no violation. In this paper, we employ separated constraint satisfaction, to deal these two objectives independently like as (...) a multi-objective optimization. The efficiency of SCS is shown on a simple benchmark and the sewerage system control problem. (shrink)

This chapter argues for a pragmatic approach toward artificial intelligence and robotics by addressing some of the specific issues encountered in laboratories, in experimental human–machine interaction contexts and in user experience research. Adopting the perspective of multi-sited ethnographic fieldwork, the chapter aims to address the multiple reconfigurations that intelligent machines and robotic systems may have on societies. Drawing on Lucy Suchman’s study of human–robot interaction, we will see that reconfigurations take place in various contexts and at various scales, inviting (...) us to extend this notion well beyond the mere scope of human–machine interaction experiments. This chapter will show that it is in fact not possible to fully grasp the social reconfigurations resulting from robotics and computer science research without considering at least three categories of problems. The first one is ontological. It concerns the ambiguous status of objects imitating vital and cognitive processes which existence affects our representations of life. The second, which concerns interaction, is about the new relations we have with these technical objects. Finally, the third concerns the way in which the increasing hybridization of socio-technical systems is participating in a reconfiguration of the community which affects not only activities but also their organization. (shrink)

Function optimization underlies many real-world problems and hence is an important research subject. Most of the existing optimization methods were developed to solve primarily unconstrained problems. Since real-world problems are often constrained, appropriate handling of constraints is necessary in order to use the optimization methods. In particular, the performances of some methods such as Genetic Algorithms can be substantially undermined by ineffective constraint handling. Despite much effort devoted to the studies of constraint-handling methods, it has been reported (...) that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. For this reason, we investigated the guidelines for a method to effectively handle constraints. The guidelines are that the method 1) takes the approach of repair operators, 2) monotonically decreases both the number of violated constraints and constraint violations, and 3) searches over the boundaries of violated constraints. Based on these guidelines, we designed a new constraint-handling method Pareto Descent Repair operator in which ideas derived from multi-objective local search and gradient projection method are incorporated. Experiments comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR. (shrink)

I submit that epistemic progress in key areas of contemporary academic philosophy has been compromised by politically correct ideology. First, guided by an evolutionary account of ideology, results from social and cognitive psychology and formal philosophical methods, I expose evidence for political bias in contemporary Western academia and sketch a formalization for the contents of beliefs from the PC worldview taken to be of core importance, the theory of social oppression and the thesis of anthropological mental egalitarianism. Then, aided by (...) discussions from contemporary epistemology on epistemic values, I model the problem of epistemic appraisal using the frameworks of multi-objective optimization theory and multi-criteria decision analysis and apply it to politically correct philosophy. I conclude that philosophy guided by politically correct values is bound to produce constructs that are less truth-conducive and that spurious values which are ideologically motivated should be abandoned. Objections to my framework stemming from contextual empiricism, the feminine voice in ethics and political philosophy are considered. I conclude by prescribing the epistemic value of empirical adequacy, the contextual value of political diversity and the moral virtue of moral courage to reverse unwarranted trends in academic philosophy due to PC ideology. (shrink)

This paper addresses the development of explainable decision support systems (DSS) for aerospace design through the integration of human expertise and computational intelligence. Traditional machine learning methods and high-fidelity simulation models often lack transparency, which constrains engineer trust and complicates human–system interaction in safety-critical design contexts. To overcome these limitations, a combined methodological framework is considered that unifies explainable artificial intelligence (XAI), human-in-the-loop decision-making, physics-informed neural networks (PINNs), and digital twins within a coherent design support paradigm. Explainable models enable interpretable (...) assessment of the influence of design parameters on optimization outcomes, while human-in-the-loop mechanisms ensure continuous expert validation and alignment with safety regulations and operational standards. The use of PINNs guarantees physical consistency of computational models by embedding governing physical laws into data-driven representations. Digital twins further provide an interactive platform that integrates simulation results, measurement data, and expert judgments for comparative analysis of alternative design scenarios. The results demonstrate that such hybrid and explainable frameworks enhance transparency, reproducibility, and reliability of aerospace design decisions, while facilitating effective collaboration between engineers and intelligent systems. The study identifies key perspectives for future research, including deeper integration of physical modeling into explainable algorithms, advancement of visualization techniques, and the development of synergistic platforms combining digital twins and human-in-the-loop approaches for multi-objective optimization under uncertainty. (shrink)

Objectives During the COVID-19 pandemic, healthcare resources including staff were diverted from paediatric services to support COVID-positive adult patients. Hospital visiting restrictions and reductions in face-to-face paediatric care were also enforced. We investigated the impact of service changes during the first wave of the pandemic on children and young people (CYP), to inform recommendations for maintaining their care during future pandemics. Design A multi-centre service evaluation was performed through a survey of consultant paediatricians working within the North Thames Paediatric (...) Network, a group of paediatric services in London. We investigated six areas: redeployment, visiting restrictions, patient safety, vulnerable children, virtual care and ethical issues. Results Survey responses were received from 47 paediatricians across six National Health Service Trusts. Children's right to health was largely believed to be compromised by the prioritisation of adults during the pandemic (81%; n = 33). Sub-optimal paediatric care due to redeployment (61%; n = 28) and the impact of visiting restrictions on CYP's mental health (79%; n = 37) were reported. Decreased hospital attendances of CYP were associated with parental fear of COVID-19 infection-risks (96%; n = 45) and government ‘stay at home’ advice (89%; n = 42). Reductions in face-to-face care were noted to have disadvantaged those with complex needs, disabilities and safeguarding concerns. Conclusion Consultant paediatricians perceived that paediatric care was compromised during the first wave of the pandemic, resulting in harm to children. This harm must be minimised in subsequent pandemics. Recommendations for future practice which were developed from our findings are provided, including maintaining face-to-face care for vulnerable children. (shrink)

In multi-armed bandit problems, information acquired from experimentation is valuable because it tells the agent whether to select a particular option again in the future. This article tests whether people undervalue this information because they are ambiguity averse, or have a distaste for uncertainty about the average quality of each alternative. It is shown that ambiguity averse agents have lower than optimal Gittins indexes, appearing to undervalue information from experimentation, but are willing to pay more than ambiguity neutral agents (...) to learn the true mean of the payoff distribution, appearing to overvalue objectively given information. This prediction is tested with a laboratory experiment that elicits a Gittins index and a willingness to pay on six two-armed bandits. Consistent with the predictions of ambiguity aversion, the Gittins indexes are significantly lower than optimal and willingnesses to pay are significantly higher than optimal. (shrink)

The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, and community rights. This study presents a comprehensive framework to address these conflicts through data-driven optimization and ethical analysis. First, a robust data collection and modeling process is established to quantify energy demand and renewable adoption trends. Multi-objective optimization using the Multi-Objective Particle Swarm Optimization (MOPSO) and Mixed-Integer Programming (MIP) methods (...) is then applied to balance conflicting objectives. The results reveal significant improvements in energy efficiency, carbon reduction, and stakeholder satisfaction, with MOPSO demonstrating superior performance. Ethical considerations are integrated through an impact vs. satisfaction analysis, which highlights the positive correlation between ecological benefits and public acceptance. Finally, a sensitivity analysis validates the robustness of the proposed solutions under varying conditions. The findings emphasize the potential of combining advanced algorithms with ethical frameworks to design sustainable and socially equitable low-carbon energy systems. (shrink)

Member selection to form an effective collaboration new product development team is crucial for a successful NPD. Existing researches on member selection mostly focus on the individual attributes of candidates. However, under the background of collaboration, knowledge complementarity and collaboration performance among candidates are important but overlooked. In this paper, we propose a multi-objective optimization model for member selection of a Co-NPD team, considering comprehensively the individual knowledge competence, knowledge complementarity, and collaboration performance. Then, to solve the (...) model, an improved adaptive genetic algorithm is developed. Finally, a real case is provided to illustrate the application of the model, and the IAGA is implemented to select the desired team members for optimal team composition. Additionally, the standard generic algorithm and particle swarm optimization are used to compare with the IAGA to further verify the effectiveness of the IAGA. (shrink)

Strategic Environmental Assessment, as a support to strategic planning, is a starting point in the creation of a sustainable concept of managing waste that is based on the principles of a circular economy. The role of SEA is to guide the planning process towards the goal of securing the best effects in relation to the quality of the living environment and the socio-economic aspects of development. SEA is also an instrument that can be used when making optimal decisions about spatial (...) development, which further contributes to its importance and role in the planning process. The implementation of SEA allows developers to establish the benefits and implications of the proposed spatial changes, taking into account the capacity of the space to sustain the planned development, and to determine the degree of acceptability of the proposed spatial changes. This paper presents a specific method used for impact assessment in SEA for the Agro-Waste Management Plan for Oplenac Vineyard. The specificity of this method is that it combines specific goals, indicators and criteria for assessing the effect of planning solutions formulated in the simulated AWMP for Oplenac Vineyard using a semi-quantitative expert method. The results of the paper indicate the possibility of using GIS tools to increase objectivity in the expert evaluation of planning solutions, particularly in relation to a group of criteria for assessing the spatial dispersion of the impacts. This reduces the subjectivity that is characteristic of all expert methods. The graphical presentation of the results in GIS technology and the use of matrices and graphs to present the results makes them easier to understand and creates a good basis for making optimal decisions on future activities concerning the elimination of waste from wineries and viticulture. The research was carried out within the framework of the NoAW project, which is supported by the European Commission through the Horizon2020 research and innovation program. (shrink)

The SDML programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. SDML is a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability.

Existing literature on women career development focuses on western industrialized countries and gives little attention to regions where patriarchy is prevailing like in North African countries. The limited available research in the region takes a business-oriented perspective to investigate the topic and focuses on business process optimization for an increased profitability. In an attempt to close this gap, the present book takes a sociological lens to investigate meanings and interpretations that skilled women in Moroccan organizations give to their experiences. (...) This project sought to understand how Moroccan professional women take decisions about their career path while taking a multi-level approach to examine contextual factors. In this book, Bourdieu’s theory of practice was used as a theoretical framework to examine the interplay of the different factors impacting the development of women career paths. Using a qualitative research approach under the interpretivist paradigm, and drawing on in-depth interviews with 10 participants, this research investigated female and male conceptualization of career and work objectives. (shrink)

Fairness in AI has emerged as a critical priority, particularly in high-stakes domains like criminal justice, where algorithmic decisions profoundly affect individuals and demographic groups. This study addresses persistent challenges of bias in recidivism prediction by evaluating the integrated application of fairness-enhancing techniques across the AI pipeline. Specifically, we analyze the integrated use of Reweighing, Adversarial Learning, Disparate Impact Remover, Exponentiated Gradient Reduction, Reject Option Classification, and Equalized Odds optimization. These methods are assessed using key fairness metrics, Statistical Parity (...) Difference (SPD), Disparate Impact (DI), Equal Opportunity Difference (EOD), and Predictive Equality Difference (PED), on two real-world recidivism datasets: COMPAS, a well-known publicly available dataset, and RisCanvi, a curated English dataset derived from a publicly available Spanish dataset. Our findings reveal trade-offs between fairness improvements and predictive accuracy, with fairness performance varying across integrated mitigation strategies. While individual techniques often fall short of effectively reducing bias, their integration across pre-processing, in-processing, and post-processing stages demonstrates a more comprehensive and robust capacity to address fairness concerns. In some instances, fairness mitigation strategies exacerbate disparities instead of reducing them, highlighting the complexity of achieving equitable AI outcomes. Some methods struggle with specific datasets, leading to worsened SPD, DI, EOD, or PED values, potentially reinforcing biases rather than eliminating them. Using multi-objective and bi-objective optimization frameworks, we identify optimal configurations that balance fairness and accuracy, contributing to a deeper understanding of fairness–accuracy trade-offs in AI systems. This study makes a novel contribution by systematically evaluating multi-phase fairness integration in recidivism prediction, bridging critical gaps in the literature. However, our results also underscore the risks of ineffective or counterproductive fairness interventions when not carefully tailored. Our findings highlight the necessity of tailoring bias mitigation strategies to specific datasets and contexts, providing actionable insights for developing equitable, diverse, and inclusive AI systems that are fair and reliable in the criminal justice system. (shrink)

The Next Release Problem (NRP) is a combinatorial optimization problem that aims to find a subset of software requirements to be delivered in the next software release, which maximize the satisfaction of a list of clients and minimize the effort required by developers to implement them. Previous studies have applied various metaheuristics, mostly genetic algorithms. Estimation of Distribution Algorithms (EDA), based on probabilistic modelling, have been proved to obtain good results in problems where genetic algorithms struggle. In this paper (...) we propose to adapt three EDAs to tackle the multi-objective NRP in a fast and effective way. Results show that EDAs can be applicable to solve the NRP with rather good quality of solutions. Furthermore, we prove that their execution time can be significantly reduced using a per-iteration solution subset selection method while maintaining the overall quality of the solutions obtained, and they perform the best when limiting the search time as in an interactive tool that requires fast responsiveness. The experimental framework, code and datasets have been made public in a code repository. (shrink)

En este trabajo se presenta una comparación de los tiempos de respuesta, optimización de la ruta y complejidad del grafo en métodos de planificación de trayectoria para robots móviles autónomos. Se contrastan los desarrollos de Voronoi, Campos potenciales, Roadmap probabilístico y Descomposición en celdas para la navegación en un mismo entorno y validándolos para un número variable de obstáculos. Las evaluaciones demuestran que el método de generación de trayectoria por Campos Potenciales, mejora la navegación respecto de la menor ruta obtenida, (...) el método Rapidly Random Tree genera los grafos de menor complejidad y el método Descomposición en celdas, se desempeña con menor tiempo de respuesta y menor coste computacional. Palabras Clave: optimización, trayectoria, métodos de planificación, robots móviles. Referencias [1]H. Ajeil, K. Ibraheem, A. Sahib y J. Humaidi, “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, ” Applied Soft Computing, vol. 89, April 2020. [2]K.Patle, G. Babu, A. Pandey, D.R.K. Parhi y A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Defence Technology, vol. 15, pp. 582-606, August 2019. [3]T. Mack, C. Copot, D. Trung y R. De Keyser, “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, vol. 86, pp. 13-28, December 2016. [4]L. Zhang, Z. Lin, J. Wang y B. He, “Rapidly-exploring Random Trees multi-robot map exploration under optimization framework,” Robotics and Autonomous Systems, vol. 131, 2020. [5]S. Khan y M. K. Ahmmed, "Where am I? Autonomous navigation system of a mobile robot in an unknown environment," 2016 5th International Conference on Informatics, Electronics and Vision, pp. 56-61, December 2016. [6]V. Castro, J. P. Neira, C. L. Rueda, J. C. Villamizar y L. Angel, "Autonomous Navigation Strategies for Mobile Robots using a Probabilistic Neural Network," IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2795-2800, Taipei, 2007. [7]Y. Li, W. Wei, Y. Gao, D. Wang y C. Fan, “PQ-RRT*: An improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, August 2020. [8]A. Muñoz, “Generación global de trayectorias para robots móviles, basada en curvas betaspline,” Dep. Ingeniería de Sistemas y Automática Escuela Técnica Superior de Ingeniería Universidad de Sevilla, 2014. [9]H. Montiel, E. Jacinto y H. Martínez, “Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes,” Información Tecnológica, vol. 26, 2015. [10] C. Expósito, “Los diagramas de Vornooi, la forma matemática de dividir el mundo,” Dialnet, Diciembre 2016. (shrink)

In terms of scenic marketing, big data research also plays an important role in the precise marketing of scenic spots. This paper has focused on the big data related to scenic spots as the research object, explores the relationship between various subdivision big data and the number of tourists in scenic spots, and investigates the difference and influence of the consumption behavior of the secondary consumption items in the scenic area, to find the potential of the scenic area’s business growth (...) and to promote the continuous and stable growth of the scenic area’s sales and tourism economy. Using the relevant theories and analysis methods, such as consumer behavior, big data, and tourism consumer behavior, the content mainly focuses on the establishment of the analysis model of the number of tourists in the scenic spot, the data collection, the estimation of the model parameters, the various types of big data, the calculation of the contribution rate of the data to the number of tourists in the scenic spot, and the difference analysis of the secondary consumption items of different types of tourists in the scenic spot. Results show that a multi-objective analysis model is established based on the relevant econometric theories, and an optimization plan is proposed after the multicollinearity diagnosis of the model; to establish a data envelopment analysis model of the difference and influence of different types of tourists’ consumption behavior in scenic spots and study the consumption behavior characteristics of different types of tourists when they purchase secondary consumption items in scenic spots; the econometric model is used to analyze the big data, adjust the linear relationship of some variables, then adopt the method of gradually adding variables combined with the consumer theory, and finally determine the number of daily tourists as the explained variable, the number of internet protocol, Baidu index, and the virtual value of the weekend, dummy variables for variables, bounce rate, and air pollution as explanatory variables. (shrink)

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization (...) problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results. (shrink)

In this paper, Taylor series is used to solve neutrosophic multi-objective programming problem (NMOPP). In the proposed approach, the truth membership, Indeterminacy membership, falsity membership functions associated with each objective of multi-objective programming problems are transformed into a single objective linear programming problem by using a first order Taylor polynomial series. Finally, to illustrate the efficiency of the proposed method, a numerical experiment for supplier selection is given as an application of Taylor series method (...) for solving neutrosophic multi-objective programming problem at end of this paper. (shrink)

Objective Moral distress is a complex phenomenon experienced by healthcare professionals. This study examined the relationships between key dimensions of Organizational Culture in Healthcare (OCHC)—perceived psychological safety, ethical climate, patient safety—and healthcare professionals’ perception of moral distress.Design Cross-sectional surveySetting Pediatric and adult critical care medicine, and adult hospital medicine healthcare professionals in the United States.Participants Physicians (n = 260), nurses (n = 256), and advanced practice providers (n = 110) participated in the study.Main outcome measures Three dimensions of OCHC (...) were measured using validated questionnaires: Olson’s Hospital Ethical Climate Survey, Agency for Healthcare Research and Quality’s Patient Safety Culture Survey, and Edmondson’s Team Psychological Safety Survey. The perception of moral distress was measured using the Moral Distress Amidst a Pandemic Survey. The hypothesized relationships between various dimensions were tested with structural equation modeling (SEM).Results Adequate model fit was achieved in the SEM: a root-mean-square error of approximation =0.072 (90% CI 0.069 to 0.075), standardized root mean square residual = 0.056, and comparative fit index =0.926. Perceived psychological safety (β= −0.357, p <.001) and patient safety culture (β = −0.428, p<.001) were negatively related to moral distress experience. There was no significant association between ethical climate and moral distress (β = 0.106, p = 0.319). Ethical Climate, however, was highly correlated with Patient Safety Culture (factor correlation= 0.82).Conclusions We used structural equation model to test a theoretical model of multi-dimensional organizational culture and healthcare climate (OCHC) and moral distress.Significant associations were found, supporting mitigating strategies to optimize psychological safety and patient safety culture to address moral distress among healthcare professionals. Future initiatives and studies should account for key dimensions of OCHC with multi-pronged targets to preserve the moral well-being of individuals, teams, and organizations. (shrink)

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent’s individual and ethical objectives. The second step consists in (...) designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour. (shrink)

This thesis investigates two main issues concerning the behavior of rational agents, preference dynamics and agent diversity. -/- We take up two questions left aside by von Wright, and later also the multitude of his successors, in his seminal book Logic of Preference in 1963: reasons for preference, and changes in preference. Various notions of preference are discussed, compared and further correlated in the thesis. In particular, we concentrate on extrinsic preference. Contrary to intrinsic preference, extrinsic preference is reason-based, i.e. (...) one's preference for one option over another has a reason. A logical model is proposed and its properties are determined. Dynamics come in naturally, since reasons for the preferences can change. Logical systems and formal results regarding dynamical preference change are then presented. -/- Preference arises from comparisons between alternatives. A first option is to compare situations. Abstractly speaking, preferences are in this case between propositions, viewed as sets of possible worlds. The reasons can then be based on a `betterness' relation over possible worlds. Propositional preference arises as a lift from this primitive relation. A standard modal logical approach is taken and we use a modality for the betterness relation in the language. We then model preference change by techniques from dynamic epistemic logic (DEL), where a typical action, e.g. a suggestion or a command can change the betterness ordering of the worlds, and thereby the propositional preference. Dynamic reduction axioms are obtained to encode exactly how such a change takes place. We obtain a complete dynamic preference logic. -/- A second option is to compare objects as such. Concretely, properties of the objects often determine the preference over the objects. Properties are now the reasons. Inspired by Optimality Theory (OT), we propose a priority sequence, an ordering of properties. Various ways of getting a preference from the priority sequence are investigated, though we mostly follow the OT approach. We use a fragment of first-order logic to describe the situation. Here, on the dynamic side, it is priority change that leads to preference change. Using the DEL methodology again we propose a complete set of reduction axioms concerning the possible dynamic operations on priority sequences. -/- Not surprisingly, the above two views are closely related. After all, possible worlds can be thought of as objects! On the basis of a systematic comparison of the two views, we develop a two level perspective, in which the models themselves are structured in layers. In particular, correspondence results between the changes at the level of the possible worlds and the changes at the level of the priority sequences are proved. We end up by sketching a two-level preferential predicate logic to describe more complex circumstances in which situations and objects are compared simultaneously. -/- But we do not see this as the whole story. Preference does not live by itself, it is often intermingled with epistemic notions of knowledge and belief. One can have different intuitions about how this entanglement operates. A few options are discussed, and proposals for logical models are presented. When moving to dynamics, we now see a picture of knowledge update, belief revision and preference change taking place symbiotically, often unconsciously as in real life. -/- The resulting picture in the thesis is one of agents that process information and adjust beliefs and preferences in many different ways. There is no logically prescribed unique norm for doing this. The second part of the thesis takes this general phenomenon of diversity of agents as its focus, since it raises many issues for logical systems and the idealized agents which they normally presuppose. In reality, agents can differ across a wide spectrum of cognitive abilities and habits: in their memory capacity, observation power, inferential power, introspective ability, and revision policies when facing new information. -/- Two kinds of agents, perfect recall agents, and memory-free agents are studied thoroughly, in the setting of playing games. We show how current dynamic logics can be `parametrized' to allow for this memory diversity, with new characterizations of agent types resulting in complete dynamic-epistemic logics. The other dimension of the reality of diverse agents is that, however different they are, they often do manage to coordinate with each other successfully. While this theme has been prominent in game theory and multi-agent systems, it has received hardly any attention in logic. We analyze this interaction between different agents by looking at concrete scenarios which model their types explicitly. -/- Finally, we analyze the issue of agent diversity in its generality, discussing what dynamic logics would have to look like to become a full-fledged account of agents of different capacities and tendencies that pursue and sometimes achieve their goals in irreducibly social settings. -/- . (shrink)

The third volume of “Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” presents an in-depth exploration of the cutting-edge developments in uncertain combinatorics and set theory. This comprehensive collection highlights innovative methodologies such as graphization, hyperization, and uncertainization, which enhance combinatorics by incorporating foundational concepts from fuzzy, neutrosophic, soft, and rough set theories. These advancements open new mathematical horizons, offering novel approaches to managing uncertainty within complex systems. Combinatorics, a discipline focused on counting, arrangement, (...) and structure, often faces challenges when uncertainty is present. Set theory, which underpins combinatorial problems, has evolved to tackle these challenges. The introduction of fuzzy and neutrosophic sets has expanded the toolkit for modeling uncertainty by incorporating elements of truth, indeterminacy, and falsehood into decision-making processes. These innovations seamlessly intersect with graph theory, providing new ways to represent uncertain structures through "graphized" forms such as hypergraphs and superhypergraphs. This volume also introduces advanced concepts like Neutrosophic Oversets, Undersets, and Offsets, which push the boundaries of classical graph theory and offer deeper insights into the mathematical and practical challenges posed by real-world systems. By blending combinatorics, set theory, and graph theory, the authors have created a robust framework for addressing uncertainty in both mathematical systems and their real-world applications. This foundation sets the stage for future breakthroughs in combinatorics, set theory, and related fields. Each chapter in this volume contributes both theoretical foundations and practical applications, demonstrating the power of integrating graph theory, set theory, and uncertainty models. The new ideas, algorithms, and mathematical tools presented here will drive the future of combinatorial research and its applications in uncertain environments. In the first chapter, “Introduction to Upside-Down Logic: Its Deep Relation to Neutrosophic Logic and Applications”, the authors present Upside-Down Logic, a novel logical framework that systematically transforms truths into falsehoods and vice versa, based on contextual shifts. Introduced by F. Smarandache, this paper provides a mathematical definition of Upside-Down Logic, including applications related to the Japanese language. The chapter also introduces Contextual Upside-Down Logic, an extension that adjusts logical connectives alongside flipped truth values, as well as Indeterm-Upside-Down Logic and Certain Upside-Down Logic to address indeterminacy. A simple algorithm is also proposed to demonstrate the computational aspects of this logic. In the second chapter, “Local-Neutrosophic Logic and Local-Neutrosophic Sets: Incorporating Locality with Applications”, the authors introduce Local-Neutrosophic Logic and Local-Neutrosophic Sets, which integrate the concept of locality into Neutrosophic Logic. By defining locality as the influence of immediate surroundings on an object or system, this chapter explores how it affects indeterminacy in real-world problems. The paper also examines potential applications and provides mathematical definitions for these new concepts. The third chapter, “A Review of Fuzzy and Neutrosophic Offsets: Connections to Some Set Concepts and Normalization Function”, extends the concept of offsets in uncertain set-theoretic frameworks, such as Fuzzy Sets, Neutrosophic Sets, and Plithogenic Sets. This chapter introduces several advanced types of offsets, including Nonstationary Fuzzy Offset, Multi-valued Plithogenic Offset, and Subset-valued Neutrosophic Offset, offering deeper insights into handling uncertainty in mathematical models. In the fourth chapter, “Review of Plithogenic Directed, Mixed, Bidirected, and Pangene OffGraph”, the authors build upon Plithogenic Graphs to propose extensions such as Plithogenic Directed OffGraph, Plithogenic BiDirected OffGraph, and Plithogenic Mixed OffGraph. These new concepts, including the Plithogenic Pangene OffGraph, are explored in detail, with a focus on their mathematical properties and potential applications in uncertain graph theory. The fifth chapter, “Short Note on Neutrosophic Closure Matroids”, explores the extension of matroid concepts into Neutrosophic and Turiyam Neutrosophic set theories, introducing Neutrosophic closure matroids. This concept integrates uncertainty, indeterminacy, and liberal states into matroid theory, enhancing its applicability in optimization and combinatorial problems. In the sixth chapter, “Some Graph Parameters for Superhypertree-width and Neutrosophic Tree-width”, the authors discuss graph parameters such as Superhypertree-width and Neutrosophic tree-width. These parameters play a crucial role in the study of graph characteristics, particularly in algorithms and real-world applications. The chapter explores the generalization of hypergraphs to SuperHyperGraphs and examines how these concepts extend tree-width parameters within the context of Neutrosophic logic. (shrink)