[1]郭玉纯,曹小鹏,胡元娇.禁忌搜索灰狼优化算法研究[J].计算机技术与发展,2019,29(12):55-60.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 010]
　GUO Yu-chun,CAO Xiao-peng,HU Yuan-jiao.Research on Tabu Search-grey Wolf Optimization Algorithm[J].,2019,29(12):55-60.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 010]
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禁忌搜索灰狼优化算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年12期
页码:
55-60
栏目:
智能、算法、系统工程
出版日期:
2019-12-10

文章信息/Info

Title:
Research on Tabu Search-grey Wolf Optimization Algorithm
文章编号:
1673-629X(2019)12-0055-06
作者:
郭玉纯; 曹小鹏; 胡元娇
西安邮电大学 计算机学院,陕西 西安 710121
Author(s):
GUO Yu-chun; CAO Xiao-peng; HU Yuan-jiao
School of Computer,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
关键词:
灰狼优化算法; 禁忌搜索算法; 局部搜索; 局部最优
Keywords:
grey-wolf-optimization; tabu search; local search; local optimum
分类号:
TP301.6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 12. 010
摘要:
灰狼优化算法是模拟灰狼捕食行为的新型智能优化算法。 原始灰狼算法由于种群迭代更新始终靠近最优解,所以存在易陷入局部最优解以及早熟收敛过快的现象。 为了解决该问题,提出了一种基于禁忌搜索的灰狼优化算法,在原始灰狼优化算法中引入禁忌表的策略。 禁忌表可以记录若干次历史搜索记录,下轮算法迭代可通过检索禁忌表来避免迂回搜索。 当算法多次迭代且无法进一步获得更优解时,对当前最优解再进行一轮禁忌搜索,使得算法在一定次数内避免再次回到历史搜索中,进而跳出局部最优。 通过对8 个 Benchmark 基准函数的寻优测试表明,改进后的算法与原始灰狼优化算法和粒子群算法相比,其全局搜索能力获得显著提高,收敛速度加快,收敛精度更高,寻优能力更佳。
Abstract:
Grey-wolf-optimization (GWO) is a new intelligent optimization algorithm which simulates predation behavior of grew wolf.The original grey wolf algorithm is always close to the optimal solution,so it is easy to fall into the local optimal solution with too fast premature convergence. Aiming at these shortcomings,we propose an improved GWO based on the tabu search where the tabu list strategy is introduced into the basic GWO. Tabu list can record several times of historical search,and the next iteration can avoid circuitous search by searching tabu list. When the algorithm iterates for many times and cannot further obtain a better solution,anotherround of tabu search is conducted for the current optimal solution,so that the algorithm can avoid returning to the historical search again within a certain number of times,and then jump out of the local optimal. The optimization test of 8 Benchmark functions shows that the improved GWO has stronger global search capability,faster convergence,higher precision,and better search capability compared with basic GWO algorithm and PSO algorithm.

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更新日期/Last Update: 2019-12-10