Particle Swarms with dynamic ring topology

Particle swarm optimizer (PSO) is a recently proposed population-based evolutionary algorithm, which exhibits good performance in many fields, and now itpsilas becoming more and more popular due to its strong global optimization capability and simple implementation. To achieve better performance, some variants investigated the utilization of different topologies in PSO. However, particles are only ldquoconceptuallyrdquo connected in the topology, and the neighborhoods of a certain particle never change (i.e. the neighborhood structure is fixed). In this paper, we propose a dynamically changing ring topology, in which particles are connected unidirectionally with respect to their personal best fitness. Meanwhile, two strategies, namely the ldquolearn from far and better onesrdquo strategy and the ldquocentroid of massrdquo strategy are used to enable certain particle to communicate with its neighbors. Experimental results on six benchmarks functions validate the effectiveness of the proposed algorithm.