In the a posteriori approach of multiobjective optimization the Pareto front is approximated by a finite set of solutions in the objective space. The quality of the approximation can be measured by different indicators that take into account the approximation's closeness to the Pareto front and its distribution along the Pareto front. In particular, the averaged Hausdorff indicator prefers an almost uniform distribution. An observed drawback of multiobjective estimation of distribution algorithms (MEDAs) is that - as common for randomized metaheuristics - the final population usually is not uniformly distributed along the Pareto front. Therefore, we propose a postprocessing strategy which consists of applying the averaged Hausdorff indicator to the complete archive of generated solutions after optimization in order to select a uniformly distributed subset of nondominated solutions from the archive. In this paper, we put forward a strategy for extracting the above described subset. The effectiveness of the proposal is contrasted in a series of experiments that involve different MEDAs and filtering techniques.
Heuristic Multiobjective Discrete Optimization using Restricted Decision Diagrams
Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Department of Operations and Information Management, University of Connecticut（康涅狄格大学运营与信息管理系）
Training-Free Diffusion-Driven Modeling of Pareto Set Evolution for Dynamic Multiobjective Optimization
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究所）；Department of Artificial Intelligence, School of Informatics, Xiamen University（厦门大学信息学院人工智能系）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education（教育部多媒体可信感知与高效计算重点实验室）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室）；Department of Artificial Intelligence, Sichuan University（四川大学人工智能系）
Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
School of Automation and Electrical Engineering, Zhongyuan University of Technology（中原文理大学自动化与电气工程学院）；School of Electrical Engineering, Zhengzhou University（郑州大学电气工程学院）
An Ontology of Preference-Based Multiobjective Metaheuristics
National University of Defense Technology（国防科技大学）；Leiden University（莱顿大学）；De Montfort University（德蒙福特大学）；Instituto Universitário de Lisboa (ISCTE-IUL)（里斯本大学学院）；Polytechnic Institute of Leiria（莱里亚理工学院）；University of Münster（明斯特大学）
RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization
Department of Engineering, University of Toyama（富山大学工学部）；Cyberscience Center, Tohoku University（东北大学网络科学中心）；Faculty of Science and Technology, Hirosaki University（弘前大学理工学部）；Sino-German College of Applied Sciences, Tongji University（同济大学中德工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Department of Information Engineering, Yantai Vocational College（烟台职业学院信息工程系）
Bayesian preference elicitation for decision support in multiobjective optimization
Institute of Applied Analysis（应用分析研究所）；Numerical Simulation , University of Stuttgart, Germany（数值模拟，斯图加特大学，德国）；Institute for Parallel（并行系统研究所）；Distributed Systems , University of Stuttgart, Germany（分布式系统，斯图加特大学，德国）；Surrogate Modeling Lab , Ghent University - imec, Belgium（替代建模实验室，根特大学-imec，比利时）；Department of Computing（计算部门）；Mathematical Sciences , California Institute of Technology, USA（数学科学，加州理工学院，美国）
