RWOA:一种新的增强鱼优化算法,用于数值优化和工程设计问题的多策略
Junhao Wei1, Yanzhao Gu1, Baili Lu2
1Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
PloS one
|April 28, 2025
概括
本研究介绍了增强的鱼优化算法 (RWOA),以克服原始鱼优化算法 (WOA) 的局限性. 在优化任务和工程应用中,RWOA表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 群集情报 群集情报 群集情报
背景情况:
- 鱼优化算法 (WOA) 是一种受欢迎的元启发算法,其灵感来源于鱼的行为.
- 佳能WOA面临的挑战包括缓慢的融合,低准确性,减少的多样性和勘探-开发不平衡.
研究的目的:
- 增强鱼优化算法 (WOA),以解决其固有的局限性.
- 提高融合速度,准确性,人口多样性以及勘探与开发之间的平衡.
主要方法:
- 提出了增强的鱼优化算法 (RWOA),包括六种新的策略.
- 利用了良好的节点集,混合协作探索,螺旋环绕猎物,并进行了飞行集成的螺旋更新.
- 实施了增强的考契突变和重新设计的参数"a"更新,以改善勘探开发平衡.
主要成果:
- 在23个基准功能中,RWOA表现出与标准WOA相比的显著改进.
- 定量分析和与最先进的算法进行比较证实了RWOA的卓越性能.
- 每个六个增强策略的影响都被单独分析.
结论:
- 增强的鱼优化算法 (RWOA) 有效地克服了原始WOA的缺陷.
- RWOA显示出解决复杂的现实世界工程设计优化问题的巨大潜力.
- 提议的改进导致了metaheuristic优化中的更好的融合,准确性和多样性.
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