萨姆德:DNDE和SPIDE算法的自适应选择与MRLDE
1REC Bijnor, Chandpur 246725, India.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
一种新的自适应差异进化 (DE) 方法SaMDE自动选择最佳突变策略以提高性能. 这提高了复杂的优化任务的效率,在基准和现实问题上表现优于其他DE变体.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进化计算是一种进化计算.
背景情况:
- 差异进化 (DE) 是一种广泛使用的优化算法.
- 适应性策略对于提高DE效率至关重要.
- 之前的工作引入了修改后的DE变体:MRLDE,DNDE和SPIDE.
研究的目的:
- 为了引入一种新的自我适应方法,SaMDE.
- 通过自动策略选择来提高修改后的DE变体的性能.
- 验证SaMDE在各种优化问题上的有效性.
主要方法:
- 设计并实施了SaMDE自适应技术.
- 集成的SaMDE与MRLDE,DNDE和SPIDE进行了整合.
- 在50个基准问题和一个分子能量最小化任务上测试了SaMDE.
主要成果:
- 与其他DE变异相比,SaMDE表现优越.
- 自适应的方法有效地选择了适当的突变策略.
- 在一系列基准功能和实际应用中验证了有效性.
结论:
- SaMDE为差异进化提供了一种强大而高效的自我适应方法.
- 该方法成功地自动化了突变策略选择,增强了优化.
- SaMDE代表了DE算法性能和适用性的重大进步.
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