Multi-population Genetic Algorithm Based on Optimal Weight Dynamic Control Learning Mechanism

Abstract

Genetic algorithm (GA) has strong global search ability and is easy to operate, but it has some disadvantages, such as slow convergence speed, and easy to fall into local extreme. In order to overcome these disadvantages, an improved genetic algorithm is proposed in this paper. Firstly, instead of the random initialization method, a uniform partition multi-population initialization method is used to generate the initial populations. This method calculates clustering centers by the criterion of Hamming distance, so as to generate different populations. The algorithm can make the initial solutions disperse in the solution space as much as possible, thus avoiding the problem of local extremes. Secondly, the ideas of multi-population parallel mechanism and learning mechanism are introduced to further improve the performance of algorithm. Based on the analysis of advantages and disadvantages of the two mechanisms, new improvements are made to these two mechanisms. Modified multi-population parallel mechanism and optimal weight dynamic control learning mechanism are proposed. In addition, the rationality of the two improved mechanisms is discussed. At last, the above mentioned two mechanisms and the new initialization method are combined. Simulation results show that the proposed algorithm has better performance in convergence speed and accuracy than other genetic algorithms.

Keywords

• genetic algorithm (ga);
• optimal weight;
• multi-population parallel mechanism;
• learning mechanism