Belief space-guided approach to self-adaptive particle swarm optimization

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm Intelligence Pub Date : 2024-01-31 DOI:10.1007/s11721-023-00232-5

Daniel von Eschwege, Andries Engelbrecht

引用次数: 0

Abstract

Particle swarm optimization (PSO) performance is sensitive to the control parameter values used, but tuning of control parameters for the problem at hand is computationally expensive. Self-adaptive particle swarm optimization (SAPSO) algorithms attempt to adjust control parameters during the optimization process, ideally without introducing additional control parameters to which the performance is sensitive. This paper proposes a belief space (BS) approach, borrowed from cultural algorithms (CAs), towards development of a SAPSO. The resulting BS-SAPSO utilizes a belief space to direct the search for optimal control parameter values by excluding non-promising configurations from the control parameter space. The resulting BS-SAPSO achieves an improvement in performance of 3–55% above the various baselines, based on the solution quality of the objective function values achieved on the functions tested.

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自适应粒子群优化的信念空间引导方法

粒子群优化（PSO）的性能对所使用的控制参数值很敏感，但针对当前问题调整控制参数的计算成本很高。自适应粒子群优化（SAPSO）算法试图在优化过程中调整控制参数，理想情况下不引入对性能敏感的额外控制参数。本文借鉴文化算法（CA），提出了一种信念空间（BS）方法，用于开发 SAPSO。由此产生的 BS-SAPSO 利用信念空间，通过排除控制参数空间中的非预期配置，引导最佳控制参数值的搜索。根据所测试功能的目标函数值的求解质量，BS-SAPSO 的性能比各种基线提高了 3-55%。

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

Swarm Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS

CiteScore

5.70

自引率

11.50%

发文量

审稿时长

>12 weeks

期刊介绍： Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental and/or practical aspects of swarm intelligence. All articles are published both in print and in electronic form. There are no page charges for publication. Swarm Intelligence is published quarterly. The field of swarm intelligence deals with systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. It is a fast-growing field that encompasses the efforts of researchers in multiple disciplines, ranging from ethology and social science to operations research and computer engineering. Swarm Intelligence will report on advances in the understanding and utilization of swarm intelligence systems, that is, systems that are based on the principles of swarm intelligence. The following subjects are of particular interest to the journal: • modeling and analysis of collective biological systems such as social insect colonies, flocking vertebrates, and human crowds as well as any other swarm intelligence systems; • application of biological swarm intelligence models to real-world problems such as distributed computing, data clustering, graph partitioning, optimization and decision making; • theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms.