Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy

Aiming at the problem that the ant colony algorithm has slow convergence rate and poor diversity in solving traveling salesman problem (TSP), double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy is proposed. Firstly, the ant colony is dynamically divided into...

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Autor principal:	MENG Jingwen, YOU Xiaoming, LIU Sheng
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	ant colony algorithm propagation factor cooperation operator collaborative mechanism dynamic regulation strategy traveling salesman problem (tsp) Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/36cc2f1a5d8643c3aaaa263da91cd0e8
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id	oai:doaj.org-article:36cc2f1a5d8643c3aaaa263da91cd0e8
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spelling	oai:doaj.org-article:36cc2f1a5d8643c3aaaa263da91cd0e82021-11-10T08:30:00ZDouble Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy10.3778/j.issn.1673-9418.20080601673-9418https://doaj.org/article/36cc2f1a5d8643c3aaaa263da91cd0e82021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2959.shtmlhttps://doaj.org/toc/1673-9418Aiming at the problem that the ant colony algorithm has slow convergence rate and poor diversity in solving traveling salesman problem (TSP), double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy is proposed. Firstly, the ant colony is dynamically divided into guide ants and cooperative ants according to fitness value, so as to form a heterogeneous double ant colony. Secondly, the heterogeneous double ant colony adopts the collaborative mechanism to balance the diversity and convergence rate of the algorithm: the guide ant introduces the propagation factor in the path construction, which increases the probability of the ant choosing a new path, expands the search range, and improves the diversity of the algorithm. The cooperative ant is guided by the optimal path of the guide ant. When the path similarity reaches the threshold, the cooperative operator is started to accelerate the convergence speed. Finally, the dynamic regulation strategy is introduced, the adaptive control operator is introduced when the global pheromone is updated, and the pheromone of the global optimal path is positively stimulated or reverse-penalized, so as to accelerate the convergence speed and avoid the algorithm falling into the local optimal. The experimental results of solving the TSP test set show that the improved algorithm not only improves the quality of solutions, ensures the diversity of algorithms, but also speeds up the convergence speed of the algorithm, especially in large-scale urban problems.MENG Jingwen, YOU Xiaoming, LIU ShengJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticleant colony algorithmpropagation factorcooperation operatorcollaborative mechanismdynamic regulation strategytraveling salesman problem (tsp)Electronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2206-2221 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	ant colony algorithm propagation factor cooperation operator collaborative mechanism dynamic regulation strategy traveling salesman problem (tsp) Electronic computers. Computer science QA75.5-76.95
spellingShingle	ant colony algorithm propagation factor cooperation operator collaborative mechanism dynamic regulation strategy traveling salesman problem (tsp) Electronic computers. Computer science QA75.5-76.95 MENG Jingwen, YOU Xiaoming, LIU Sheng Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
description	Aiming at the problem that the ant colony algorithm has slow convergence rate and poor diversity in solving traveling salesman problem (TSP), double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy is proposed. Firstly, the ant colony is dynamically divided into guide ants and cooperative ants according to fitness value, so as to form a heterogeneous double ant colony. Secondly, the heterogeneous double ant colony adopts the collaborative mechanism to balance the diversity and convergence rate of the algorithm: the guide ant introduces the propagation factor in the path construction, which increases the probability of the ant choosing a new path, expands the search range, and improves the diversity of the algorithm. The cooperative ant is guided by the optimal path of the guide ant. When the path similarity reaches the threshold, the cooperative operator is started to accelerate the convergence speed. Finally, the dynamic regulation strategy is introduced, the adaptive control operator is introduced when the global pheromone is updated, and the pheromone of the global optimal path is positively stimulated or reverse-penalized, so as to accelerate the convergence speed and avoid the algorithm falling into the local optimal. The experimental results of solving the TSP test set show that the improved algorithm not only improves the quality of solutions, ensures the diversity of algorithms, but also speeds up the convergence speed of the algorithm, especially in large-scale urban problems.
format	article
author	MENG Jingwen, YOU Xiaoming, LIU Sheng
author_facet	MENG Jingwen, YOU Xiaoming, LIU Sheng
author_sort	MENG Jingwen, YOU Xiaoming, LIU Sheng
title	Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
title_short	Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
title_full	Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
title_fullStr	Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
title_full_unstemmed	Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy
title_sort	double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/36cc2f1a5d8643c3aaaa263da91cd0e8
work_keys_str_mv	AT mengjingwenyouxiaomingliusheng doubleantcolonyalgorithmbasedoncollaborativemechanismanddynamicregulationstrategy
_version_	1718440362756800512

Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy

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