Technique for Reducing the Time Required for Local Optimum Searching in a Hybrid Genetic Algorithm

Journal of Computational Science and Technology

Boontee KRUATRACHUE¹  Supakit NOOTYASKOOL¹ 

[1] Department of Computer Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang

关键词: Hybrid Genetic Algorithm;    Diversity Selection;    Nelder-Mead Method;    Quasi-Newton Method;    Optimization Technique;   

DOI  :  10.1299/jcst.5.38

学科分类：地球科学(综合)

来源: Japan Academy

【 摘 要 】

References(28)Cited-By(1)Many applications use a combination of a local optimum search (LOS) and a genetic algorithm (GA), called a hybrid genetic algorithm (HGA), to solve problems. This hybrid can improve the performance of finding optimum solutions, but the HGA may produce redundancy when applying an LOS to inappropriate populations. This redundancy is the cause of high computation time, and it generates premature convergence and decreases HGA performance. This research therefore aims to reduce redundant LOS in HGAs. We propose a new technique called diversity selection (DS) and measure redundancy when applying an LOS in HGA. In this work, the DS selects appropriate populations to which to apply an LOS. The experiment then compares DS with other HGAs on numerical optimization problems. In addition, the HGAs were tested on two LOSs, a Nelder-Mead method and a quasi-Newton method to compare the speed of finding the optimum point in an LOS. The experimental results show that DS was able to quickly find the optimum point and give fewer redundant LOSs than other HGAs.

【 授权许可】

Unknown