Hybrid evolutionary search method for complex function optimisation problems

作     者：Binol, H. Guvenc, I. Bulut, E. Akkaya, K. 

作者机构：Florida Int Univ Dept ECE Miami FL 33199 USA NC State Univ Dept ECE Raleigh NC USA Virginia Commonwealth Univ Dept CS Richmond VA USA 

出 版 物：《ELECTRONICS LETTERS》 (电子学快报)

年 卷 期：2018年第54卷第24期

页      面：1377-1378页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Qatar National Research Fund, QNRF, (NPRP9-257-1-056) Qatar National Research Fund, QNRF 

主　　题：genetic algorithms search problems estimation theory hybrid evolutionary search method complex function optimisation problems harmony search technique genetic algorithm GA HS direction estimation mechanism genetic operators HS algorithm local optimum solutions elitism crossover benchmark functions hybridisation approach mutation 

摘      要：In this Letter, harmony search (HS) technique hybridised with genetic algorithm (GA) is proposed. This technique mainly takes HS direction estimation mechanism and genetic operators in GA, which significantly increase the convergence of the HS algorithm. Specifically, the authors propose to incorporate main operators of GA into the HS algorithm to avoid some inherent drawbacks of the HS. For example, crossover is incorporated into HS to deal with low accuracy problem, while mutation is incorporated to escape from the local optimum solutions. In addition, elitism is introduced into the HS, to precipitate the performance and prevent the loss of favourable individuals found during the search process. The authors compare the performance of the GA, HS, and other popular HS variants on several benchmark functions. Numerical results show that the proposed hybridisation exhibits a superior performance in comparison to other algorithms.