Meta-PSO for Multi-Objective EM Problems

Recently, the Particle Swarm Optimization (PSO) method has been successfully applied to many different electromagnetic optimization problems. Due to the complex equations, usually calling for a numerical solution, the associated cost function is in general very computationally expensive. A fast convergence of the optimization algorithm is hence of paramount importance to attain results in an acceptable time. In this chapter few variations over the standard PSO algorithm, referred to as Meta-PSO, aimed to enhance the global search capability, and, therefore, to improve the algorithm convergence, are analyzed. The Meta-PSO class of methods can be furthermore subdivided into the Undifferentiated and a Differentiated subclasses, whether the law updating particle velocity is the same for all particles or not, respectively. In recently published open literature the results of the application of the Meta-PSO to the optimization of single-objective problems have been shown. Here we will prove their enhanced properties with respect to standard PSO also for the optimization of multi-objective problems, trough their test in multi-objective benchmarks and multi-objective optimization of an antenna array.