A New Hybrid PSO-based Genetic Algorithm and its Application to Lay- out Problems

Layout problems belong to NP-Complete problems theoretically. They are concerned more and more in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, plant equipments, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are relatively effective to solve these kind of problems. But usually there still exist two main defects, i.e. premature convergence and slow convergence rate. To overcome them, a new improved hybrid PSO-based genetic algorithm (HPSO-GA) is proposed on the basis of parallel genetic algorithms (PGA). In this algorithm, chaos initialization, hybrid strategy and multisubpopulation evolution based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure can prevent the algorithm from premature in the early stage and benefit accelerating convergence in the late stage as well. 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). An example of layout problems shows that HPSO-GA is feasible and effective.