A Robust Adaptive Hybrid Genetic Simulated Annealing Algorithm for the global optimization of multimodal functions
In this paper we presented a novel hybrid genetic algorithm for solving NLP problems based on combining the Genetic algorithm and Simulated annealing, together with a local search strategy. The proposed hybrid approach combines the merits of genetic algorithm (GA) with simulated annealing (SA) to construct a more ef.cient genetic simulated annealing (GSA) algorithm for global search, which could well maintain the population diversity in GA evolution without becoming easily trapped in local optimum. The iterative hill climbing (IHC) method as a local search technique is incorporated into GSA loop to speed up the convergence of the algorithm. In addition, a self-adaptive hybrid mechanism is developed to maintain a tradeoff between the global and local optimizer searching then to ef.ciently locate quality solution to complex optimization problem. The computational results indicate that the global searching ability and the convergence speed of this hybrid algorithm are signi.cantly improved. Some well-known benchmark functions are utilized to test the applicability of the proposed algorithm.
genetic simulated annealing iterative hill climbing (IHC) method adaptive scheme global optimization
Qiaoling Xu Gongwang Zhang Chao Zhao Aimin An
Faculty of College of Chemistry and Chemical Engineering, FuZhou University, FuZhou,350108, P.R.Chin College of Chemistry and Chemical Engineering, FuZhou University, FuZhou, 350108, P.R.China Faculty of School of Electrical Engineering and Information Engineering, Lanzhou University of Techn
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
7-12
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）