多子种群微粒群免疫算法及其在函数优化中应用

A Multi-Subpopulation PSO Immune Algorithm and Its Application on Function Optimization

为克服基本微粒群算法的早熟问题,借鉴多子种群和自适应的思想,提出了基于两层模型的多子种群自适应多态杂交微粒群免疫算法.该算法首先通过对若干个子种群进行低层自适应多态杂交微粒群操作,改善了子种群的多样性,有效抑制了收敛过程中的早熟停滞现象;然后通过高层免疫克隆选择操作,显著地提高了全局寻优能力,进一步提高了收敛精度.针对函数优化的仿真结果表明:与其他改进微粒群算法相比,该算法具有更快的收敛速度和更高的求解精度,尤其适合高维及多模态优化问题的求解.

Basic particle swarm optimization （PSO） algorithm, which is a global and parallel optimization of high performance, simplicity, robustness, no problem specific information, etc., has been widely used in computer science, optimization of scheduling, function optimization and other fields. However, the basic PSO algorithm has the defects of premature convergence, stagnation phenomenon and slow convergence speed in the later evolution period for complex optimization problems. In order to overcome the premature convergence problem of basic PSO algorithm, using idea of multi-subpopulation and self-adaptive for reference, a novel multi-subpopulation adaptive polymorphic crossbreeding particle swarm optimization immune algorithm （MAPCPSOI） based on two-layer model is proposed. Through the bottom layer adaptive polymorphic crossbreeding PSO operation of several subpopulations, the MAPCPSOI algorithm, firstly, could ameliorate diversity of subpopulation distribution and effectively suppress premature and stagnation behavior of the convergence process. Secondly, the MAPCPSOI algorithm, by the top layer immune clonal selection operation of several subpopulations, could significantly improve the global optimization performance and further enhance the convergence precision. Compared with other improved PSO algorithms, simulated results of function optimization show that the MAPCPSOI algorithm, especially suitable for solving high-dimension and multimodal optimization problems, has rapider convergence speed and higher solution precision.