A New Variation of Adaptive Simulated Annealing for 2D/3D Packing Optimization

Abstract

2D/3D packing optimization is facing big challenges to get better solution with less runtime. In this paper, we propose a new variation of adaptive simulated annealing (ASA) to solve packing problem. In the traditional ASA, the parameters that control temperature scheduling and random step selection are adjusted according to search progress. In the proposed ASA, a guide with adaptive probabilities is used to automatically select moving methods, including crossover to improve its efficiency. The interesting point is the traditional SA with crossover is inefficient, while the proposed ASA with crossover is efficient due to the adaptive guide. Based on the experiment using MCNC, ami49_X and ami98_3D benchmarks, the computational performance is considerably improved. In the case of area minimization, the results gotten by the proposed ASA are normally better than the published data of 2D packing. In the case of volume minimization for 3D packing, the results gotten by the proposed ASA are better than the data of traditional ASA and SA.