【摘要】 提出了一种改进的非支配排序遗传算法。通过扩大第一代种群规模,在初期加速种群的进化;对选择算子引入概率操作来提高种群的多样性;同时引入混合交叉算子,动态调节算法的搜索空间。最后以收敛性和分布性作为性能指标,使用公开的多目标测试函数对其进行测试,并与基本的非支配排序遗传算法和改进的多目标粒子群算法进行比较。实验结果表明,改进后的非支配排序遗传算法在收敛性和分布性两方面均有提升。更多还原

【Abstract】 An improved non-dominated sorting genetic algorithm is proposed in this paper. By expanding the scale of the first generation population,the evolution of the population is accelerated in the early stage; Probability operation is introduced to the selection operator to improve the divevsity of the population. A hybrid crossover operator is used to adjust search space dynamically. Finally,with convergence and diversity as performance indicators,the algorithm is tested with open multi-objective test function,and compared with the basic non-dominated sorting genetic algorithm and the improved multi-objective particle swarm optimization algorithm. The experimental results show that the improved non-dominated sorting genetic algorithm improves both convergence and diversity.更多还原