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

Abstract

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.