双子人口竞争性粒子群集优化与工程应用
Shuying Zhang1, Yufei Zhang2, Minghan Gao3
1College of Computer Science and Technology, Beihua University, Jilin 132013, China.
Biomimetics (Basel, Switzerland)
|February 26, 2026
概括
本研究介绍了双子群竞争粒子群优化 (DCPSO),这是一个平衡勘探和开发的自适应算法. 通过动态重新分配资源,DCPSO提高了对复杂问题的粒子群优化性能.
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:
- 粒子集群优化 (PSO) 是一种受欢迎的生物灵感算法.
- 在公用事业任务中平衡勘探和开采是一个持续的挑战.
- 现有的PSO变种经常使用静态策略,限制了适应性,并冒着过早融合的风险.
研究的目的:
- 提出一个适应性的PSO变体,解决勘探-开发平衡问题.
- 通过动态资源配置来提高优化性能和稳定性.
- 为了引入双子群竞争性粒子集群优化 (DCPSO).
主要方法:
- 将PSO群体划分为不同的勘探和开发子群体.
- 实施一个动态的竞争机制来评估搜索进展.
- 利用基于性能的子群体之间的适应性粒子迁移.
主要成果:
- 与标准PSO和其他最先进的算法相比,DCPSO表现出更高的性能.
- 在大多数CEC2017基准函数中观察到统计学上显著的改进,特别是混合和组成问题.
- 在工程设计问题上,DCPSO表现出稳健性,稳定的融合性和实际有效性.
结论:
- 通过动态分群管理,DCPSO有效平衡勘探和开采.
- 拟议的适应性迁移策略增强了计算资源的分配.
- DCPSO为复杂的优化任务提供了强大而有效的替代方案.
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