一个改进的混合鱼优化算法用于全球优化和工程设计问题
Abolfazl Rahimnejad1, Ebrahim Akbari2, Seyedali Mirjalili3,4
1Department of Mechanical Engineering, McMaster University, Hamilton, Canada.
PeerJ. Computer science
|December 11, 2023
概括
一个新的Pbest引导差分鱼优化算法 (PDWOA) 通过结合来自多个解决方案的信息来改进原来的鱼优化算法 (WOA). 这种增强的混合优化方法在基准和现实世界的工程问题中表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 鱼优化算法 (WOA) 是一个流行的元启发法,但它只依赖最好的解决方案限制了它的探索能力.
- 现有的优化方法往往难以有效地平衡勘探和开发.
研究的目的:
- 引入一种新的混合算法,Pbest引导差异WOA (PDWOA),提高WOA的性能.
- 通过整合粒子群优化 (PSO) 和微分演化 (DE) 的概念来解决标准WOA的局限性.
主要方法:
- 开发了Pbest引导差异性WOA (PDWOA) 算法.
- 使用30维CEC2014基准函数进行全面评估.
- 在现实世界的工程设计问题上进行测试:压力容器,张力/压缩弹和束优化.
主要成果:
- 与原始WOA和其他近期方法相比,PDWOA显示出显著的性能改进.
- 使用威尔科克森签名等级和弗里德曼测试的统计验证证了PDWOA的有效性.
- 成功应用于复杂的基准和现实世界的优化任务.
结论:
- 拟议的PDWOA算法提供了一种优越的混合优化方法.
- PDWOA有效地克服了标准WOA的局限性,提供了更好的勘探和开发.
- 这项研究强调了混合元启发学在复杂的优化挑战中的潜力.
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