多模态问题上粒子群优化算法的虚拟位置引导策略

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chao Li, Jun Sun, Li-Wei Li, Min Shan, Vasile Palade, Xiaojun Wu
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引用次数: 0

摘要

对于粒子群优化(PSO)算法来说,过早收敛是一个棘手的问题,尤其是在多模式问题上,保持粒子群的多样性至关重要。然而,大多数 PSO 增强策略,包括现有的多样性引导策略,都没有完全解决这个问题。本文提出了 PSO 算法的虚拟位置引导(VPG)策略。VPG 策略计算两个不同种群的多样性值,并建立多样性基线。然后,它根据这些多样性值和基线之间的关系,通过发散、正常和加速三个阶段,在每次迭代中动态指导算法进行不同的搜索行为。这些阶段共同协调不同的方案,以平衡探索和利用,共同引导算法远离局部最优,提高解决方案的质量。虚拟位置 "的引入满足了该策略对各种 PSO 算法的适应性,确保了所提出的 VPG 策略的通用性和有效性。只需一个超参数和推荐的常规设置,VPG 即可轻松实现。实验结果表明,VPG 策略优于几种典型策略和最先进的多样性引导策略,并能有效提高大多数 PSO 算法在不同维度的多模态问题上的搜索性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Virtual Position Guided Strategy for Particle Swarm Optimization Algorithms on Multimodal Problems.

Premature convergence is a thorny problem for particle swarm optimization (PSO) algorithms, especially on multimodal problems, where maintaining swarm diversity is crucial. However, most enhancement strategies for PSO, including the existing diversity-guided strategies, have not fully addressed this issue. This paper proposes the virtual position guided (VPG) strategy for PSO algorithms. The VPG strategy calculates diversity values for two different populations and establishes a diversity baseline. It then dynamically guides the algorithm to conduct different search behaviors, through three phases-divergence, normal, and acceleration-in each iteration, based on the relationships among these diversity values and the baseline. Collectively, these phases orchestrate different schemes to balance exploration and exploitation, collaboratively steering the algorithm away from local optima and towards enhanced solution quality. The introduction of "virtual position" caters to the strategy's adaptability across various PSO algorithms, ensuring the generality and effectiveness of the proposed VPG strategy. With a single hyperparameter and a recommended usual setup, VPG is easy to implement. The experimental results demonstrate that the VPG strategy is superior to several canonical and the state-of-the-art strategies for diversity guidance, and is effective in improving the search performance of most PSO algorithms on multimodal problems of various dimensionalities.

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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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