计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
5期
58-64,250
,共8页
粒子群算法%质心%自适应指数惯性权重
粒子群算法%質心%自適應指數慣性權重
입자군산법%질심%자괄응지수관성권중
particle swarm optimization algorithm%centroid%self-adaptive exponential inertia weight
针对标准粒子群优化算法易出现早熟收敛及寻优精度低等缺陷，提出一种基于双质心和自适应指数惯性权重的改进粒子群算法（DCAEPSO）。算法使用粒子搜到的最优解和当前解构造加权的种群质心和最优个体质心，结合使用自适应指数惯性权重调整了速度更新公式。通过几个典型测试函数仿真及Friedman和Holm检验，实验结果显示DCAEPSO比其他粒子群算法寻优能力强。
針對標準粒子群優化算法易齣現早熟收斂及尋優精度低等缺陷，提齣一種基于雙質心和自適應指數慣性權重的改進粒子群算法（DCAEPSO）。算法使用粒子搜到的最優解和噹前解構造加權的種群質心和最優箇體質心，結閤使用自適應指數慣性權重調整瞭速度更新公式。通過幾箇典型測試函數倣真及Friedman和Holm檢驗，實驗結果顯示DCAEPSO比其他粒子群算法尋優能力彊。
침대표준입자군우화산법역출현조숙수렴급심우정도저등결함，제출일충기우쌍질심화자괄응지수관성권중적개진입자군산법（DCAEPSO）。산법사용입자수도적최우해화당전해구조가권적충군질심화최우개체질심，결합사용자괄응지수관성권중조정료속도경신공식。통과궤개전형측시함수방진급Friedman화Holm검험，실험결과현시DCAEPSO비기타입자군산법심우능력강。
This paper proposes a new Particle Swarm Optimization(PSO)algorithm based on two aspects of improvement in standard PSO to avoid the problems about premature convergence and low precision. It adjusts velocity updating formula by embedding self-adaptive exponential inertia weight function and two weighted centroids, which are called the popula-tion centroid and the best individual centroid. Through the simulation of several typical benchmark functions, Friedman’s tests and Holm’s tests, the experimental results indicate that the proposed algorithm not only has advantages of conver-gence property over standard PSO and some other modified PSO algorithms, but also outperforms other algorithms pro-posed in this paper for searching global optimal solution.
