虚拟位置指导的粒子群优化策略 在多模式问题上的算法
Chao Li1, Jun Sun2, Li-Wei Li3
1Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu 214122, China lcmeteor@hotmail.com.
Evolutionary computation
|May 22, 2024
概括
虚拟位置引导 (VPG) 策略通过保持群体多样性来增强粒子群体优化 (PSO),以防止在复杂问题上过早地趋同. 这种方法可以提高解决方案质量和算法性能.
科学领域:
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
- 优化算法 优化算法
背景情况:
- 过早的融合是粒子群集优化 (PSO) 的一个重大挑战,特别是对于需要持续群集多样性的多式问题.
- 现有的以多样性为导向的战略往往无法完全解决公共服务任务中过早融合的问题.
- 保持群体多样性对于PSO算法中的有效探索和利用至关重要.
研究的目的:
- 提出一种新的虚拟位置引导 (VPG) 策略,以解决粒子群优化 (PSO) 的过早融合问题.
- 通过改善群体多样性,提高PSO算法的多式联网优化问题的性能.
- 开发一种适用于各种公共服务组织算法的通用和可适应的战略.
主要方法:
- 虚拟位置指导 (VPG) 策略计算了两个人群的多样性值,并建立了一个多样性基线.
- 它根据多样性值和基线动态引导算法通过三个阶段 (分歧,正常,加速).
- 该策略平衡了探索和利用,以引导算法远离局部最佳.
主要成果:
- 实验结果表明,VPG策略的表现优于正规和最先进的多样性指导策略.
- VPG策略有效地提高了大多数PSO算法的多式联络问题的搜索性能.
- 该战略证明了多式联运问题的各种维度的有效性.
结论:
- 拟议的虚拟定位指导 (VPG) 策略是提高粒子群集优化 (PSO) 性能的一种优质方法.
- 通过动态管理群体多样性和平衡勘探-开发,VPG有效地减轻过早的融合.
- 该战略的适应性和易于实施使其成为对群众情报研究的有价值贡献.
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