Adaptive Particle Swarm Optimization with Dynamic Population and its Application to Constrained Engineering Design Optimization

Abstract

This paper presents a variant of the particle swarm optimization (PSO) that we call the adaptive particle swarm optimization with dynamic population (DP-APSO), which adopts a novel dynamic population (DP) strategy whereby the population size of swarm can vary with the evolutionary process. The DP strategy enables the population size to increase when the swarm converges and decrease when the swarm disperses. Experiments were conducted on two well-studied constrained engineering design optimization problems. The results demonstrate better performance of the DP-APSO in solving these engineering design optimization problems when compared with two other evolutionary computation algorithms.

Multi-objective optimization design method of naval vessels degaussing coils

“…In order to maximize the efficiency of the degaussing system, a large number of degaussing coils are firstly laid on the model in this paper, and then LARS 13 algorithm is used to sort the coils according to the contribution of coils for the magnetic abnormal signals generated by ships under specific conditions. In practical application, a certain number of windings are selected from the multiple windings from the front to the back to obtain the windings combination mode that is most suitable for compensating the current magnetic abnormal signal.…”