一个修改的差异进化算法基于改进一个新的突变策略和自我适应交叉
Sadeer Fadhil1, Hegazy Zaher2, Naglaa Ragaa1
1Operations Research, Faculty of Graduate Studies for Statistical Research.
MethodsX
|December 15, 2023
概括
本研究介绍了差异进化算法 (DE) 的新型突变策略,提高了它的效率. 经过修改的DE算法在各种优化问题中在CPU时间,评估和准确性方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 差异进化算法 (DE) 是一个广泛认可的基于人口的元启发.
- 最近的研究重点是提高DE的性能和适用性.
研究的目的:
- 为差异进化算法提出一个新的突变策略,DE/current-to-best/2,用于差异进化算法.
- 引入一种自适应交叉程序,根据代数调整局部.
- 使用实验设计优化修改后的DE算法的参数.
主要方法:
- 开发了一种新的突变策略 (DE/current-to-best/2),利用最佳和当前矢量之间的距离加上随机矢量.
- 实现了一种自我适应的交叉机制,根据代平价在高和低局域之间切换.
- 使用实验设计进行了参数优化.
- 修改后的DE算法与经典的DE在11个不同的优化问题上进行了基准测试.
主要成果:
- 拟议的DE/current-to-best/2战略与自适应交叉车显著提高了性能.
- 对比结果表明,在CPU时间,评估数量和解决方案准确性方面,效率更高.
- 经过修改的算法比经典的DE版本显示出更高的融合和稳定性.
结论:
- 新型突变和自我适应的交叉策略提高了差异进化算法的有效性.
- 优化的DE算法为解决复杂的优化问题提供了更有效,更准确的方法.
- 这种修改后的DE算法代表了自然启发的元启发性优化中的宝贵进步.
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