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A novel grey prediction evolution algorithm for multimodal multiobjective optimization

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104173-104173页

作者： Zhou, Ting Hu, Zhongbo Zhou, Quan Yuan, Shixiong Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

ABSTR A C T In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE?s effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF.

关键词： Grey prediction evolution algorithm multimodal multiobjective evolutionary algorithms multimodal multiobjective optimization problems

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A clustering differential evolution algorithm with neighborhood-based dual mutation operator for multimodal multiobjective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期216卷

作者： Zhou, Ting Hu, Zhongbo Su, Qinghua Xiong, Wentao Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China

multimodal multiobjective optimization problems (MMOPs) possess multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). Numerous multimodal multiobjective evolutionary al-gorithms (MMEAs) have been developed to solve MMOPs. However, how to locate multiple high quality equivalent PSs and obtain a uniformly distributed PF simultaneously remains a challenging task. This paper explores a novel multimodal multiobjective differential evolution algorithm, termed MMOcDE, for solving MMOPs. In MMOcDE, a neighborhood-based dual mutation strategy is proposed by developing two novel mutation operators. The two novel mutation operators provide a better tradeoff between exploration and exploitation in locating multiple equivalent PSs. In addition, a clustering-based environmental selection mechanism is designed by integrating an affinity propagation method. The affinity propagation method improves the distribution of solutions in decision and objective spaces, and a harmonic averaged distance is adopted to promote the diversity of a population. The proposed MMO_CDE is compared against state-of-the-art MMEAs on a test suite of CEC 2019 benchmark functions and a map-based practical application. Empirical results validate the superiority of the proposed MMOcDE regarding locating multiple equivalent PSs and obtaining a well-distributed PF.

关键词： Differential evolution multimodal multiobjective optimization problems multimodal multiobjective evolutionary algorithms

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