.png){kind=icon}
返回
Chaotic bat swarm optimisation (CBSO)
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2014.10.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Bat swarm optimisation (BSO) is a novel heuristic optimisation algorithm that is being used for solving different global optimisation problems. The paramount problem in BSO is that it severely suffers from premature convergence problem, that is, BSO is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BSO to mitigate this problem. Ergodicity and non-repetitious nature of chaotic functions can diversify the bats and mitigate premature convergence problem. Eleven different chaotic map functions along with various chaotic BSO strategies are investigated experimentally and the best one is chosen as the suitable chaotic strategy for BSO. The results of applying the proposed chaotic BSO to different benchmark functions vividly show that premature convergence problem has been mitigated efficiently. Actually, chaotic-based BSO significantly outperforms conventional BSO, cuckoo search optimisation (CSO), big bang-big crunch algorithm (BBBC), gravitational search algorithm (GSA) and genetic algorithm (GA). (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Bat swarm optimisation
Global optimisation
Chaos
Heuristics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
暂无机构信息
引用论文
.png){kind=icon}
Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC's in power systems增强型leader PSO (ELPSO): 一种在电力系统中分配分布式TCSC的新算法
PREAI
分享收藏An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry一种有效的混合免疫爬山优化方法，用于解决工业中的设计和制造优化问题
PREAI
分享收藏A new design optimization framework based on immune algorithm and Taguchi's method基于免疫算法和田口方法的新设计优化框架
PREAI
分享收藏
查看更多内容