一个增强的饥饿游戏搜索优化与应用到受约束的工程优化问题
Yaoyao Lin1, Ali Asghar Heidari1, Shuihua Wang2
1Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
这项研究通过引入逻辑螺旋与基于对立的学习 (LS-OBL) 和动态罗森布鲁克方法 (RM) 策略来增强饥饿游戏搜索 (HGS) 优化器. 改进的RLHGS算法在基准测试和现实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:
- 饥饿游戏搜索 (HGS) 是一个无梯度,以人口为基础的优化器,灵感来自于社会动物的食.
- HGS面临的局限性包括不充分的多样性,过早的融合和局部最佳敏感性.
- 提高HGS对于在复杂的优化任务中更广泛应用至关重要.
研究的目的:
- 引入和评估两种新的适应性策略,以改进原始的HGS算法.
- 通过整合对数螺旋与基于对立的学习 (LS-OBL) 和动态的罗森布罗克方法 (RM) 来开发一个增强的算法,RLHGS.
- 评估RLHGS的性能与基准函数和现实世界工程问题的最先进算法对比.
主要方法:
- 制定了LS-OBL战略,以减少搜索空间并保持人口多样性,增强勘探.
- 整合了动态的罗森布罗克方法 (RM) 来调整搜索方向和步骤大小,帮助逃离局部最佳.
- 结合了LS-OBL和RM,创建了RLHGS算法,以提高收率和精度.
主要成果:
- 实验证实,LS-OBL和RM显著提高了HGS的能力.
- 在23个基准函数和CEC2020测试套件中,RLHGS在23个基准函数和CEC2020测试套件中超过了8个最先进的算法.
- RLHGS有效地解决了四个受约束的现实世界工程优化问题,证明了实际的实用性.
结论:
- 拟议的RLHGS算法,集成LS-OBL和RM,提供卓越的优化性能.
- RLHGS有效地解决了原始HGS的局限性,显示了增强的多样性,融合和全球搜索能力.
- 对于理论和实际的工程挑战,RLHGS被证明是一种高度有效和高效的优化方法.
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