Affiliations: Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
Correspondence:
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Corresponding author: Amit Kumar Das, Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India. E-mail: amit.besus@gmail.com.
Abstract: Genetic algorithm (GA) is one of the most widely used meta-heuristic optimization tools to solve a variety of problems. It is a powerful tool for global optimization. However, like other heuristic optimization techniques, it has a probabilistic guarantee to reach the globally optimum solution in a finite number of generations. In addition, a GA suffers from the poor local search capability and hence, it is slow in convergence. To overcome these disadvantages of a GA, there had been a lot of attempts made by several researchers using different techniques. One of such techniques is the restart strategy for a GA. This strategy is nothing but to start with a new population of solutions of the GA, whenever it is unable to find any better quality of solution or it satisfies some conditions pre-specified by the user. There are a few restart strategies available in the literature with their own merits and demerits. In this paper, a novel restart strategy with elitism has been proposed. It consists of mainly three different conditions, and if any of these conditions is fulfilled, the algorithm gets restarted. The proposed restart strategy has been designed in such a way that a proper balance between the exploration and exploitation can be maintained during the search. In addition, the proposed restart scheme is able to detect the premature convergence situation of a GA and it triggers out a restart of the algorithm to avoid such situation. To measure the performance of a real-coded genetic algorithm (RCGA) with the proposed restart strategy, two sets of experiments have been done on different test functions, and the results are compared with that of the RCGAs with other restart strategies available in the literature. From the experiments, it is clear that the RCGA with the proposed restart strategy is able to yield the better results compared to others.
