群集智能优化算法的典型改进方法综述
A review of typical improvement methods of swarm intelligent optimization algorithms
张文雅 1赵健1
作者信息
- 1. 辽宁科技大学 理学院,辽宁 鞍山 114051
- 折叠
摘要
元启发式群集智能优化算法通过模拟自然现象或生物行为来寻找问题的最优解,是一类成功且具有竞争力的全局优化方法.本文概述了近几年典型的元启发式群集智能优化算法及其设计原理;详细介绍了其中4类典型改进方法:种群初始化、增添新策略、迭代公式调整、算法混合;对元启发式群集智能优化算法未来的改进和发展进行了展望.
Abstract
Meta-heuristic swarm intelligent optimization algorithms are successful and competitive global opti-mization methods.They find globally optimal solutions by simulating natural phenomena or biological behav-iors.In this paper,the typical meta-heuristic swarm intelligent optimization algorithms and their design princi-ple are introduced.Secondly,four typical improvement methods of this kind of optimization algorithm are summarized in detail.There are population initialization,adding new strategies,iterative formula adjustment,and algorithm mixing.Finally,the future improvement and development of meta-heuristic swarm intelligent optimization algorithms are prospected.
关键词
元启发式/群集智能优化算法/优化性能/改进方法Key words
meta-heuristic/swarm intelligent optimization algorithm/optimize performance/improvement method引用本文复制引用
基金项目
国家自然科学基金(U1731128)
辽宁省自然科学基金(2019-MS-174)
辽宁省教育厅项目(LJKZ0279)
出版年
2024
NETL
NSTL
万方数据