改进的差分进化算法以最佳和最差的位置为指导 探索动态 探索动态
1ASH (Mathematics) Department, REC Bijnor, Chandpur 246725, UP, India.
Biomimetics (Basel, Switzerland)
|February 23, 2024
概括
本研究介绍了一种用最佳和最差位置 (IDEBW) 算法改进的差分演化算法. IDEBW通过引导探索向最佳解决方案和远离次优化解决方案来增强进化计算,提高效率.
科学领域:
- 进化计算是一种进化计算.
- 优化算法 优化算法
背景情况:
- 探索搜索空间对于进化算法至关重要.
- 差异进化 (DE) 使用交叉和突变进行探索.
- 现有的DE方法可以改进,以便更好地进行位置勘探.
研究的目的:
- 为差异进化算法提出一个新的探索策略.
- 引入最佳和最差位置 (IDEBW) 的改进DE算法.
- 通过引导式勘探提高DE的效率.
主要方法:
- 开发了一种最佳和最坏的位置导向勘探方法.
- 实施了最佳和最差位置的改进DE (IDEBW).
- 评估了IDEBW性能与其他DE变体和元启发术相比.
主要成果:
- IDEBW在探索新地点方面表现出卓越的表现.
- 该算法在42个基准函数 (IEEE CEC-2017) 和3个现实应用 (IEEE CEC-2011) 上进行了测试.
- 对比分析证实了IDEBW与现有方法相比的有效性.
结论:
- 拟议的IDEBW算法提供了一个更有利的勘探策略.
- IDEBW成功地提高了差异进化算法的效率.
- 这种新的方法有效地引导人们寻找更好的解决方案.
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