Performance Tuning of Evolutionary Algorithms Using Particle Sub Swarms

Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. This paper proposes a new particle swarm variant which deals with sub-populations. This algorithm is applied for solving the well known class of mathematical problems: geometrical place problems (also known as locus problems). Finding the geometrical place can be sometimes a hard task and in almost all situations the geometrical place consists in more than one single point. The performance of the sub-swarm based PSO method is compared with Evolutionary Algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm by combining PSO and EA. This combination is able to detect the geometrical place very fast for difficult problems for which EA's need more time and PSO technique even with sub-populations could not find the geometrical place