对不同的随机加速粒子群优化和松鼠搜索算法的比较评估,用于选择性波消除问题
Muhammad Ayyaz Tariq1, Muhammad Salman Fakhar2, Ghulam Abbas3
1Department of Electrical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan. ayyaztariq@hotmail.com.
随机化类型显著影响自然启发的算法,如加速粒子群优化 (APSO) 和松鼠搜索算法 (SSA) 选择性波消除 (SHE). 不同的随机化产生了不同的优化结果和解决方案质量.
科学领域:
- 计算智能是一种计算智能.
- 电气工程 电气工程
- 优化算法 优化算法
背景情况:
- 当初猜测未知时,自然启发的元启发算法是有价值的.
- 随机初始化对于这些算法的有效部署至关重要.
- 选择性波消除 (SHE) 需要强大的优化技术.
研究的目的:
- 分析不同类型的随机化对元启发算法性能的影响.
- 评估随机化如何影响选择性波消除 (SHE) 中的解决方案.
- 为了比较加速粒子群优化 (APSO) 和松鼠搜索算法 (SSA) 中各种随机化的有效性.
主要方法:
- 在APSO和SSA应用了五种不同的随机化类型 (指数式,正常,雷利,均,韦布尔).
- 利用这些算法来解决选择性波消除 (SHE) 问题.
- 进行统计分析以评估随机化的影响.
主要成果:
- 应用的随机化类型明显影响算法操作.
- 不同的随机化导致最适合的目标函数值的变化.
- 随机化的选择影响了获得SHE解决方案的质量.
结论:
- 随机化策略是SHE.metaheuristic优化成功的一个关键因素.
- 算法性能和解决方案质量对所使用的特定随机分布是敏感的.
- 对特定问题的最佳随机化进行进一步研究是有必要的.
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