Improved ant colony optimization algorithm for the traveling salesman problems

Gan, Rongwei; Qingsheng, Guo; Chang, Huiyou; Yi, Yang

doi:10.3969/j.issn.1004-4132.2010.02.025

Cited by 79 publications

(41 citation statements)

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“…In addition, in the optimization process, the pheromone is applied to the traversed edges in accordance with the length of the route to preserve the accumulated experience about optimal solutions [9][10][11][12].…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

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Modeling of the process of territorial communities formation using swarm intelligence algorithms

Lytvyn

Uhryn

Nadiein

et al. 2017

TAPR

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Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

“…The mathematical model of the problem consists in the formation in the given region of capable territorial communities, minimizing the function (11) under the constraints (6)- (10). The solution of the obtained MOP consists in choosing the optimal solution for an admissible set of solutions.…”

Section:   mentioning

confidence: 99%

Modeling of the process of territorial communities formation using swarm intelligence algorithms

Lytvyn

Uhryn

Nadiein

et al. 2017

TAPR

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“…It has also been shown that, although adding a local searcher is a good approach in the majority of cases, it may prevent ACO from finding the optimal solution [23]. A very interesting hybridization of ACO is given in article [24] were scout ants, which search the solution space in a more systematic way, are added to the algorithm. Multi-colony systems [25] have been developed, as well as variations of the basic ACO like elitist ant colony, rank based ant colony system and min-max ant system (MMAS) to improve the performance on the TSP [26] in a more natural way, with different pheromone strategies.…”

Section: Introductionmentioning

confidence: 99%

Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem

Tuba

Jovanović²

2013

INT J COMPUT COMMUN

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Abstract:A new, improved ant colony optimization algorithm with novel pheromone correction strategy is introduced. It is implemented and tested on the traveling salesman problem. Algorithm modification is based on undesirability of some elements of the current best found solution. The pheromone values for highly undesirable links are significantly lowered by this a posteriori heuristic. This new hybridized algorithm with the strategy for avoiding stagnation by leaving local optima was tested on standard benchmark problems from the TSPLIB library and superiority of our method to the basic ant colony optimization and also to the particle swarm optimization is shown. The best found solutions are improved, as well as the mean values for multiple runs. The computation cost increase for our modification is negligible.

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“…Because ants move according to the intensity of pheromones, the richer the pheromone trail on a path is, the more likely it would be followed by other ants. Hence, ants can construct the shortest way from their nest to the food sources and back [5]. This ants' behavior inspired researchers to build an algorithmic framework that uses artificial ants to solve combinational optimization problems [6].…”

Section: Ant Colony System Algorithm (Acs) For Tspmentioning

confidence: 99%

An Improved Ant Colony Algorithm Based on 3-Opt and Chaos for Travelling Salesman Problem

Yu¹,

You²,

Liu³

2014

IJCI

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This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial optimization problems. The existing algorithms still have some imperfections, we use a combination of two different operators to improve the performance of algorithm in this work. First, 3-opt local search is used as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions. Therefore, it's better and more effective algorithm for TSP.

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Improved ant colony optimization algorithm for the traveling salesman problems

Cited by 79 publications

References 0 publications

Modeling of the process of territorial communities formation using swarm intelligence algorithms

Modeling of the process of territorial communities formation using swarm intelligence algorithms

Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem

An Improved Ant Colony Algorithm Based on 3-Opt and Chaos for Travelling Salesman Problem

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