Chaotic genetic algorithm based on clustering and memory for solving dynamic optimization prblems

Most of the problems in the real world are of dyanamic optimization ones. It means that their optima may change over time, thus algorithms to solve these problems must be well adapt to their conditions in such a way that they should be able to track the optima during evolution. This article proposes a chaotic genetic algorithm based on clustering and memory for solving dynamic optimization problems. A chaotic system has more precise prediction of the future in comparison with random system and increases the speed of convergounce in the algorithm. The utilization of some information from the past allows quickly adapting right after a change. Thus the underlining idea of the paper is the use of memory in this field, which is a good strategy to store the useful information and retrieves them for reuse in the future. The clustering method maintains diversity in the memory and the population during running of the algorithm by exchanging information between corresponding clusters (clusters with similar tag) of the memory and the population. This algorithm uses a k-means clustering method to maintain diversity and improve local search. In this paper used tow the main innovation: clustering method and memory update. To test the effectiveness of the proposed method we choose the Moving Peaks Benchmark that has similar behaviors to real world dynamic proplems. The experimental results show the efficiency of the proposed algorithm for solving optimization problems in comparison with other methods.