Dynamic cluster in particle swarm optimization algorithm

Abstract

Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on the comportment of the optimization method. In this study, we propose DCluster: a dynamic topology, based on a combination of two well-known topologies viz. Four-cluster and Fitness. The proposed topology is analyzed, and compared to six other topologies used in the standard PSO algorithm using a set of benchmark test functions and several well-known constrained and unconstrained engineering design problems. Our comparisons demonstrate that DCluster outperforms the other tested topologies and leads to satisfactory performance while avoiding the problem of premature convergence.

Effect of the Dynamic Topology on the Performance of PSO-2S Algorithm for Continuous Optimization

“…Dcluster is a dynamic topology that works as follows [1]. At each iteration, the particles are sorted in a list according to their personal best fitness in increasing order, so that the worst particle has an index equal to 1 in the list (the size of the list is equal to the size of the swarm).…”