A strategy cooperative algorithm based on state-awareness for large-scale multi-objective optimization

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-09-29 DOI:10.1016/j.swevo.2025.102165

Hao Liu , Jinhua Zheng , Yaru Hu , Xiaozhong Yu , Junwei Ou , Juan Zou , Shengxiang Yang

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引用次数: 0

Abstract

Large-scale multi-objective optimization problems (LSMOPs) usually involve hundreds to thousands of decision variables. When dealing with unconstrained 2-3-objective LSMOPs, multi-objective evolutionary algorithms (MOEAs) are likely to get trapped in local optima, making it difficult to ensure the diversity and convergence of solutions within limited computational resources. To tackle this challenge, we propose a strategy-cooperative algorithm based on state-awareness for large-scale multi-objective optimization, abbreviated as LMOEA-SC. In LMOEA-SC, we have designed a state-aware mechanism that can monitor the evolutionary state of the population in real-time. Based on the real-time information, LMOEA-SC flexibly switches and collaborates between the proposed learning strategy based on diversity protection competitive swarm optimization (DPCSO) and the escape strategy based on global exploration sampling (GES), thus effectively coping with different evolutionary states and challenges. The obtained statistical results, with a 73% improvement, clearly show that compared with six state-of-the-art MOEAs, LMOEA-SC has significant competitiveness in numerous large-scale multi-objective test instances with up to 2,000 decision variables.

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基于状态感知的大规模多目标优化策略协同算法

大规模多目标优化问题（LSMOPs）通常涉及数百到数千个决策变量。多目标进化算法（moea）在处理无约束的2-3目标LSMOPs时，容易陷入局部最优，难以在有限的计算资源下保证解的多样性和收敛性。为了解决这一挑战，我们提出了一种基于状态感知的大规模多目标优化策略合作算法，简称为LMOEA-SC。在LMOEA-SC中，我们设计了一种状态感知机制，可以实时监控种群的进化状态。基于实时信息，LMOEA-SC在基于多样性保护竞争群体优化（DPCSO）的学习策略和基于全局探索采样（GES）的逃逸策略之间灵活切换和协作，有效应对不同的进化状态和挑战。所获得的统计结果提高了73%，清楚地表明，与6个最先进的moea相比，LMOEA-SC在多达2000个决策变量的大量大规模多目标测试实例中具有显著的竞争力。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.