基于共差矩阵的种群多样性机制的差异演变
1Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou Key Lab of Advanced Energy Storage and Conversion, Zhejiang Province Key Lab of Leather Engineering, College of Chemistry and Materials Engineering, Wenzhou University, Wenzhou 325035, PR China.
ISA transactions
|July 10, 2023
概括
一个新的共变矩阵差异演化 (CM-DE) 算法通过提高人口多样性和本地搜索能力来增强全球搜索. 这种改进的差异演化方法在复杂的优化问题上提供了在准确性和融合速度方面具有竞争力的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 启发式搜索 启发式搜索
背景情况:
- 差异进化 (DE) 是一种基于人口的启发式全球搜索算法,有效用于连续域问题.
- 标准DE可以在复杂的优化任务中与不足的本地搜索能力和过早的趋同到本地最佳状态作斗争.
- 现有的DE变种在维持人口多样性和实现强大的趋同方面经常面临挑战.
研究的目的:
- 提出一个改进的差分进化算法,称为共变矩阵DE (CM-DE),解决局部搜索和过早融合的局限性.
- 通过新的参数适应和扰乱策略,增强人口多样性和融合速度.
- 通过利用人口协差矩阵信息来监测个体相似性来防止局部最佳情况.
主要方法:
- 引入了新的参数适应策略:使用波形基函数 (早期阶段) 和考希分布 (后期阶段) 更新尺度因子F;通过正常分布生成交叉率CR.
- 将扰乱策略纳入交叉操作员,以加强DE算法的搜索功能.
- 构建了一个人口协差矩阵,使用其差异作为个体相似性的指标,以减轻低人口多样性问题.
主要成果:
- 在CEC2013,CEC2014,CEC2017测试套件中的88个基准函数上,CM-DE表现优于最先进的DE变体 (LSHADE,jSO,LPalmDE,PaDE,LSHADE-cnEpSin).
- 在CEC2017 50D优化中,CM-DE在30个基准函数中在22-28个方面超过了其他算法.
- 对于CEC2017 30D优化,CM-DE在30个基准函数中的19个显示了更快的趋同,并验证了其在现实应用中的可行性.
结论:
- 拟议的CM-DE算法有效地提高了人口多样性和本地搜索能力,克服了传统DE的局限性.
- 与领先的DE变体相比,CM-DE在解决方案准确性和融合速度方面表现出高度竞争力的性能.
- 算法的可行性和有效性通过对基准函数和现实世界的应用进行严格的测试来证实.
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