现代信息科技2024，Vol.8Issue(2)：60-64,5.DOI:10.19850/j.cnki.2096-4706.2024.02.014
基于种群熵偏移平均加权的改进量子粒子群算法
Improved Quantum Particle Swarm Optimization Based on Population Entropy Offset Mean Weighting
周治伟1
作者信息
- 1. 华东电子工程研究所,安徽 合肥 230031
- 折叠
摘要
Abstract
The Quantum Particle Swarm Optimization has better global search ability and is considered an extremely effective improvement to the Particle Swarm Optimization.However,there is still a problem of population diversity decay during its operation.In order to further enhance the global optimization ability of Quantum Particle Swarm Optimization,an improved Quantum Particle Swarm Optimization based on population entropy offset mean weighting is proposed,which is based on Quantum Particle Swarm Optimization in weighted mean optimal position.Dynamically associating the population entropy with the weighted range center offset value effectively enhances the traversal of the algorithm's search space and avoids premature convergence of the algorithm.By applying conventional test functions,a comparative analysis is conducted with traditional Particle Swarm Optimization,Quantum Particle Swarm Optimization,and weighted Quantum Particle Swarm Optimization,demonstrating the effectiveness of the improved algorithm proposed in the paper.关键词
量子粒子群算法/加权量子粒子群算法/种群熵/偏移平均加权/测试函数Key words
QPSO/WQPSO/population entropy/offset mean weighting/test function分类
信息技术与安全科学引用本文复制引用
周治伟..基于种群熵偏移平均加权的改进量子粒子群算法[J].现代信息科技,2024,8(2):60-64,5.
