基于自适应搜索的免疫粒子群算法

Immune particle swarm optimization algorithm based on the adaptive search strategy

经典粒子群算法由于多样性差而陷入局部最优,从而造成早熟停滞现象.为克服上述缺点,本文结合人工免疫算法,提出一种基于自适应搜索的免疫粒子群算法.首先,该算法改善了浓度机制;然后由粒子最大浓度值来控制子种群数目以充分利用粒子种群资源;最后对劣质子种群进行疫苗接种,利用粒子最大浓度值调节接种疫苗的搜索范围,不仅避免了种群退化现象,而且提高了算法的收敛精度和全局搜索能力.仿真结果表明该算法求解复杂函数优化问题的有效性和优越性.

The particle swarm algorithm is often trapped in a local optimum due to poor diversity,resulting in a premature stagnation phenomenon. In order to overcome this shortcoming,an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly,the concentration mechanism was improved. Secondly,in order to make full use of the resources of the particle population,the number of particles of sub-populations was controlled by the maximum concentration of particles. Finally,the inferior sub-populations were vaccinated,and the maximum concentration of particles was used to control the search range of the vaccine,so the population degradation was avoided,and the convergence accuracy and the global search ability of the algorithm were improved. Simulation results show the effectiveness and superiority of the proposed algorithm in solving the complex function optimization problems.