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

作　　者：张文雅 赵健[1] ZHANG Wenya;ZHAO Jian(School of Science,University of Science and Technology Liaoning,Anshan 114051,China)

机构地区：[1]辽宁科技大学理学院,辽宁鞍山114051

出　　处：《辽宁科技大学学报》2024年第2期129-137,共9页Journal of University of Science and Technology Liaoning

基　　金：国家自然科学基金资助项目(U1731128);辽宁省自然科学基金资助项目(2019-MS-174);辽宁省教育厅项目(LJKZ0279)。

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。Meta-heuristic swarm intelligent optimization algorithms are successful and competitive global optimization methods.They find globally optimal solutions by simulating natural phenomena or biological behaviors.In this paper,the typical meta-heuristic swarm intelligent optimization algorithms and their design principle 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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]