基于灰狼优化器的旅行竞赛问题的改进启发式方法

D. Gupta, Chand Anand, Tejas Dewan
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引用次数: 3

摘要

本文提出了一种改进的启发式灰狼优化算法和模拟退火算法来求解旅行比赛问题的最优解。在本文中,我们研究了http的镜像版本。我们使用一种快速的建设性启发式算法来生成调度。在此基础上,结合灰狼优化器的猎物接近模型,提出了一种改进的启发式模拟退火方法,以获得成本最小的比赛计划。我们利用概率方法改进了GWO的位置更新步骤,并将其与SA混合求解TTP,避免陷入局部极小值。我们提出的混合算法收敛于TTP的最优解。我们计算了TTP的总成本,并将我们的算法与其他元启发式算法(如MBBO/ESA、BBO/SA、ACO和PSO)的性能进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced heuristic approach for Traveling Tournament Problem based on Grey Wolf Optimizer
This paper shows an enhanced heuristic approach of grey wolf optimizer and simulated annealing in order to find optimum solution for Travelling Tournament Problem. In this paper, we tackle the mirrored version of TTP. We use a fast constructive heuristic algorithm to generate schedules. Later we integrate an enhanced heuristic approach of simulated annealing based on prey proximity model of Grey Wolf Optimizer to obtain least cost tournament schedule. We upgrade the position updating step of GWO by using probabilistic methods and hybridize it with SA to solve TTP and avoid getting stuck in local minima. Our proposed hybrid algorithm converges to an optimum solution for TTP. We calculate the overall cost of TTP and compare the performance of our alorithm with other metaheuristic algorithms like MBBO/ESA, BBO/SA, ACO and PSO.
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