OPTIMIZATION BY SIMULATED ANNEALING - AN EXPERIMENTAL EVALUATION .2. GRAPH-COLORING AND NUMBER PARTITIONING

• 成果类型: Article
• 署名作者: JOHNSON, DS; ARAGON, CR; MCGEOCH, LA; SCHEVON, C
• 署名单位: University of California System; University of California Berkeley; Amherst College; Johns Hopkins University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.39.3.378
• 发表日期: 1991
• 页码: 378-406
• 关键词: Mathematics; combinatorics; NUMBER PARTITIONING HEURISTICS; NETWORKS GRAPHS; heuristics; GRAPH COLORING HEURISTICS; simulation; Applications; OPTIMIZATION BY SIMULATED ANNEALING

摘要

This is the second in a series of three papers that empirically examine the competitiveness of simulated annealing in certain well-studied domains of combinatorial optimization. Simulated annealing is a randomized technique proposed by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi for improving local optimization algorithms. Here we report on experiments at adapting simulated annealing to graph coloring and number partitioning, two problems for which local optimization had not previously been thought suitable. For graph coloring, we report on three simulated annealing schemes, all of which can dominate traditional techniques for certain types of graphs, at least when large amounts of computing time are available. For number partitioning, simulated annealing is not competitive with the differencing algorithm of N. Karmarkar and R. M. Karp, except on relatively small instances. Moreover, if running time is taken into account, natural annealing schemes cannot even outperform multiple random runs of the local optimization algorithms on which they are based, in sharp contrast to the observed performance of annealing on other problems.