A Sub-boundary Approach for Enhanced Particle Swarm Optimization and Its Application to the Design of Artificial Magnetic Conductors

The particle swarm algorithm is a newly introduced method for electromagnetic optimization problems that is based on the observation of swarm intelligence and particle behavior. This paper proposes a novel strategy for the initialization of the agents' position within the multidimensional solution domain. In particular, the domain is initially subdivided into subdomains so to have a more uniform distribution of the agents. At a second stage, the sub-boundaries are removed and the best position information of each group is passed to each agent; the agents are therefore allowed to explore the whole search space. This procedure results to be efficient and to improve the convergence rate. A comparison between the performance of this new implementation and that of the basic particle swarm algorithm is presented for several test cases. Finally, this new procedure is successfully applied to the synthesis of artificial magnetic conductors (AMCs)