摘要
提出了一种新颖的自适应串行小生境粒子群优化算法(ASNPSO),它使用多个子群能够串行发现多个最优解.在此算法中,使用了山谷函数以决定如何改变当前运行的子群中粒子的适应度函数,算法具有很强的自适应搜索能力.经使用几个标准测试函数证明了ASNPSO算法在没有任何先验知识的情况下能够有效地发现多个最优解.
This paper proposes a novel adaptive sequential niche particle swarm optimization （ASNPSO） algorithm, which uses multiple sub-swarms to detect optimal solutions sequentially. The hill valley function was used to determine how to change the fitness of a particle in current sub-swarm run. This algorithm has a strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is efficient in searching for multiple optimal solutions for benchmark test functions without any prior knowledge.
关键词
遗传算法
小生境技术
粒子群优化
罚函数
多模函数优化
Genetic Algorithm
Niche Technique
Particle Swarm Optimization
Penalty Function
timodal Function Optimization
