一种基于免疫选择的粒子群优化算法

粒子群算法是一种新的群体智能算法,被广泛用于各种复杂优化问题的求解,但算法存在着过早收敛问题.为了克服算法早熟的缺点,将粒子群看作是一个复杂的免疫系统,借鉴生物学中免疫系统自我调节的机制,提出了一种新的基于免疫选择的粒子群优化算法(IS-PSO).免疫系统中的抗原、抗体和亲和度分别对应了待优化函数的最优解、候选解和适应度.IS-PSO通过免疫算法中免疫记忆、疫苗接种、免疫选择等操作有效地调节PSO算法中种群的多样性.给出了算法的详细步骤,并将本文提出的算法与基本的粒子群算法(bPSO)在几个典型Benchmark函数的优化问题应用中进行了比较,仿真结果表明:IS-PSO算法可以有效避免早熟问题,提高粒子群算法求解复杂函数的全局优化性能.

A novel particle swarm optimization based on immune selection

Particle swarm optimization (PSO),a novel swarm intelligence algorithm,is proved to be a valid optimization technique and has been applied in many areas successfully. However,like other evolutionary algorithms,PSO also suffered from the premature convergence problem,especially for the large scale and complex problems. In order to overcome the shortcoming,this paper regards the swarm as a complex immune system,uses for reference from the self-adjustment mechanism of immune system,and proposes a novel PSO bas...