水准点(测量)
模拟退火
数学优化
计算机科学
粒子群优化
趋同(经济学)
组合优化
人口
群体行为
算法
数学
经济增长
社会学
人口学
经济
地理
大地测量学
作者
Kenneth Brezinski,Michael Guevarra,Ken Ferens
出处
期刊:International Journal of Software Science and Computational Intelligence
[IGI Global]
日期:2020-04-01
卷期号:12 (2): 74-86
被引量:12
标识
DOI:10.4018/ijssci.2020040105
摘要
This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.
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