An Improved Multi-population Cooperative Adaptive Differential Evolution Algorithm and its Application
ZHOU Di
Abstract：For the low global searching ability and convergence speed of differential evolution algorithm(DE)in high-dimen?sion complex function optimization,the different searching strategy and parallel evolution mechanism are used,a dynamic multiple populations parallel self-adaptive differential evolution algorithm with multiple strategies(MSDPIDE)is proposed to optimize func?tions in this paper. In MSDPIDE algorithm,the population is dynamically divided into multi-populations individuals according to the difference of individuals＇fitness. Multiple strategies in multiple populations＇parallel evolution are used to improve the individu?als＇diversity for avoiding premature convergence and ensure the efficiency and sufficiency information exchanging among sub-popu?lations. In addition to,self-adaptive adjustment is introduced to automatically adjust the scaling factor and crossover factor during the running time. The MSDPIDE algorithm is tested on ten complex benchmark functions. The experiment results are compared with DE and CADE algorithms. The compared results show that the MSDPIDE algorithm takes on better searching accuracy,convergence speed,stability,remarkable global convergence ability,it is better in the searching precision,convergence speed,stability,re?markable global convergence ability,and it can avoid premature convergence effectively.
Keywords：differential evolutionmultiple populations cooperationlocal-search strategyself-adaptiveHigh-dimension?al function optimization
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1648-1651,1718 )