In this paper a new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) will be presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with two typical problems, a purely mathematical one, the search for the global maximum of a multi-dimensional sine function and an electromagnetic application, the optimization of a linear array
Genetical Swarm Optimization: a new hybrid evolutionary algorithm for electromagnetic applications
GRIMACCIA, FRANCESCO;MUSSETTA, MARCO;ZICH, RICCARDO
2005-01-01
Abstract
In this paper a new effective optimization algorithm suitably developed for electromagnetic applications called genetical swarm optimization (GSO) will be presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the particle swarm optimization (PSO) and genetic algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The algorithm is tested here with respect to the other optimization techniques dealing with two typical problems, a purely mathematical one, the search for the global maximum of a multi-dimensional sine function and an electromagnetic application, the optimization of a linear arrayI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
