针对标准遗传算法易陷入局部最优而出现早熟,提出了一种基于捕食搜索策略的遗传算法。该算法在进化中模拟动物捕食搜索的过程,并根据种群中个体最优适应值来动态改变交叉和变异概率,从而加强算法的全局搜索和局部优化的能力。仿真实验表明该算法是有效的。
To improve performance of genetic algorithm and avoid trapping to local optima, proposed a new genetic algorithm based on predatory search strategy. It simulated the animal predatory search in running, and adjusted the crossover and mutation probability by the best individual fitness in every generation. It could enhance the ability of global exploring and local search. The simulation results demonstrate the effectiveness and practicability of this algorithm.