A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems

Abstract

Layout problems belong to NP(non-deterministic polynomial)-Complete problems theoretically. They are paid more and more attention in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, shipping, vehicle and robots, plant equipments, platforms of marine drilling well. The algorithms based on swarm intelligence are relatively effective to solve this kind of problems. But usually there still exist two main defects of them, i.e. premature convergence and slow convergence rate. To overcome them, a novel improved hybrid PSO-based genetic algorithm (HPSO-GA) is proposed on the basis of parallel genetic algorithms (PGA). In this algorithm, chaos initialization and multi-subpopulation evolution based on improved adaptive crossover and mutation are adopted. And more importantly, in accordance with characteristics of different classes of subpopulations, different modes of PSO update operator are introduced. It aims at making full use of the fast convergence property of particle swarm optimization (PSO). The proposed adjustable arithmetic-progression rank-based selection can prevent the algorithm from premature in the early stage and benefit accelerating convergence in the late stage as well. An example of layout problems shows that HPSO-GA is feasible and effective.