Research on Genetic Algorithm Based on Particle Swarm Algorithm
基于粒子群算法的遗传算法研究

Premature convergence and weak local optimization are two key problems existing in the conventional genetic algorithm.To overcome the shortcomings,this paper proposes an improved genetic algorithm based on the particle swarm algorithm.The basic principle is that a new mutation operator is constructed and population is divided into parts.Three typical multimodal values functions are optimized and evaluate the efficiency of the algorithm.The experimental results show,the improved genetic algorithm can not only maintain effectively the polymorphism in the colony and avoid premature,but also greatly improve the convergent speed.