A Study of Constrained Layout Optimization Using Adaptive Particle Swarm Optimizer

The optimal layout problem of circle group in a circular container with performance constraints of equilibrium belong to an NP-hard problem. Due to its complexity, the general particle swarm optimization algorithm converges slowly and easily converges to local optima. Taking the layout problem of satellite cabins as background, a novel adaptive particle swarm optimizer is presented based on multi-modified strategies, which can not only escape from the attraction of local optima of the later phase to heighten particle diversity, and avoid the premature problem, but also maintain the characteristic of fast speed search in the early convergence phase to get global optimum. Thus, the algorithm has a better search performance to deal with the constrained layout optimization problem. Experimental results on three examples show that this algorithm is feasible and efficient.