摘要
蚁群算法和粒子群算法是最典型的2种群智能算法,各具特色和优势,已成功应用于诸多领域,但上述算法也存在一些缺陷。阐述了蚁群算法和粒子群算法分别与遗传算法、模拟退火算法、差分演化算法的各种混合策略。在算法中采用一定策略混合其他优化技术,可以提高算法的运算速度和计算精度。
As the two typical swarm intelligence algorithms,ant colony algorithm and particle swarm optimization algorithm are simple and effective which have already been applied successively in many areas,but there are some defects in them.These two algorithms mixed with other optimization technology not only can overcome the defects of the algorithms but also can increase the performance of the algorithms.This papermainly discusses the mixed strategies of these two algorithms with genetic algorithm,simulated annealing algorithm and differential evolution respectively.It can be used for improving the speed of calculation.
出处
《长江大学学报（自然科学版）》
CAS
2011年第12期76-78,10,共3页
Journal of Yangtze University(Natural Science Edition)
基金
广东省自然科学基金项目(101754539192000000)
关键词
蚁群算法
粒子群算法
遗传算法
模拟退火
差分演化
混合策略
ant colony algorithm
particle swarm optimization
genetic algorithm
simulated annealing
differential evolution
mixed strategies
