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Exploring a Promising Region and Enhancing decision space Diversity for Multimodal Multi-Objective Optimization

Tsinghua Science and Technology 2024年第2期29卷 325-342页

作者： Fei Ming Wenyin Gong School of Computer Science China University of GeosciencesWuhan 430074China

During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space ***,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space *** overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling *** traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving ***,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding *** on the above methods,we propose a novel dual-population-based coevolutionary *** studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different *** effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.

关键词： multimodal multi-objective optimization evolutionary algorithms promising region neighbor distance decision space coevolution

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Aggregation of Priorities in Multi-Criteria decision Analysis (MCDA): Connecting decision spaces in the Cognitive space

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American Journal of Operations Research 2016年第4期6卷 317-333页

作者： Sajjad Zahir Faculty of Management University of Lethbridge Lethbridge Canada

In Multi-Criteria decision Analysis, the well-known weighted sum method for aggregating normalised relative priorities ignores the unit of scale that may vary across the criteria and thus causes rank reversals. A new aggregation rule that explicitly includes the norms of priority vectors is derived and shown as a remedy for it. An algorithmic procedure is presented to demonstrate how it can as well be used in the Analytic Hierarchy Process when norms of priority vectors are not readily available. Also, recursion relations connecting two decision spaces with added or deleted alternatives give an opportunity to extend the idea of connectivity to a new concept of cognitive space. Expanded analytic modelling embracing multiple decision spaces or scenarios may assist in detecting deficiencies in analytic models and also grasping the big picture in decision making.

关键词： Aggregation Normalization decision space Cognitive space Analytic Hierarchy Process Scenario

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A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm

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Science China(Information Sciences) 2019年第7期62卷 73-89页

作者： Yi HU Jie WANG Jing LIANG Kunjie YU Hui SONG Qianqian GUO Caitong YUE Yanli WANG School of Electrical Engineering Zhengzhou University Industrial Technology Research Institute Zhengzhou University Computer Science and Information Technology School of Science RMIT University

Multi-objective optimization algorithms have recently attracted much attention as they can solve problems involving two or more conflicting objectives effectively and efficiently. However, most existing studies focus on improving the performance of the solutions in the objective spaces. This paper proposes a novel multimodal multi-objective pigeon-inspired optimization(MMOPIO) algorithm where some mechanisms are designed for the distribution of the solutions in the decision spaces. First, MMOPIO employs an improved pigeon-inspired optimization(PIO) based on consolidation parameters for simplifying the structure of the standard PIO. Second, the self-organizing map(SOM) is combined with the improved PIO for better control of the decision spaces, and thus, contributes to building a good neighborhood relation for the improved ***, the elite learning strategy and the special crowding distance calculation mechanisms are used to prevent premature convergence and obtain solutions with uniform distribution, respectively. We evaluate the performance of the proposed MMOPIO in comparison to five state-of-the-art multi-objective optimization algorithms on some test instances, and demonstrate the superiority of MMOPIO in solving multimodal multi-objective optimization problems.

关键词： multi-objective multimodal decision space pigeon-inspired optimization PIO self-organizing map SOM

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