Swarm-based intelligent optimization approach for layout problem

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.