A neighborhood-assisted evolutionary algorithm for multimodal multi-objective optimization

IF 3.3 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Memetic Computing Pub Date : 2024-04-12 DOI:10.1007/s12293-024-00410-w

Weiwei Zhang, Jiaqiang Li, Guoqing Li, Weizheng Zhang

引用次数: 0

Abstract

Multi-modal multi-objective optimization problems (MMOPs) involve multiple Pareto sets (PSs) in decision space corresponding to the same Pareto front (PF) in objective space. The difficulty lies in locating multiple equivalent PSs while ensuring a well-converged and well-distributed PF. To address this, a neighborhood-assisted reproduction strategy is proposed. Through interactions with non-dominated solutions, the generated offspring could spread out along the PF, while ineractions with neighbors could improve the convergence ability. Importantly, individuals can participate in multiple neighborhoods, reducing the computational burden. Additionally, a neighborhood-assisted environmental selection strategy is prposed to encourage exploration of diverse solution regions, ensuring a balanced distribution of the population and preservation of multiple PSs. Comparative experiments are implemented on the CEC 2019 MMOPs test suite, and the superior performance of the proposed algorithm is demonstrated in comparison to several state-of-the-art approaches.

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多模式多目标优化的邻域辅助进化算法

多模式多目标优化问题（MMOPs）涉及决策空间中与目标空间中同一帕累托前沿（PF）相对应的多个帕累托集（PSs）。问题的难点在于如何找到多个等效的帕累托集，同时确保帕累托前沿的良好融合和分布。为此，我们提出了一种邻域辅助复制策略。通过与非主导解的互动，产生的后代可以沿着 PF 扩散，而与邻近解的不互动则可以提高收敛能力。重要的是，个体可以参与多个邻域，从而减轻计算负担。此外，还提出了一种邻域辅助环境选择策略，以鼓励探索不同的解区域，确保种群的均衡分布并保留多个 PS。在 CEC 2019 MMOPs 测试套件上进行了对比实验，与几种最先进的方法相比，证明了所提算法的优越性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Memetic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

6.80

自引率

12.80%

发文量

期刊介绍： Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems. The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics: Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand. Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.