自适应粒子群优化:收敛性的综述与分析

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

Swarm Intelligence Pub Date : 2017-11-28 DOI:10.1007/s11721-017-0150-9

Kyle Robert Harrison, A. Engelbrecht, B. Ombuki-Berman

引用次数: 79

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Self-adaptive particle swarm optimization: a review and analysis of convergence

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.