A Cooperative Ant Colony System and Genetic Algorithm for TSPs

Dong, Gaifang; Guo, Wenqiang

doi:10.1007/978-3-642-13495-1_73

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…However, the running time was more than doubled over that found for 300 iterations. Other studies have reported that the global optimal is reached for several TSPs using various hybrid algorithms combining ACO with GA [8], [9], but the running time will be significantly increased. In real applications, the decision becomes if it is worth of spending more time in the hope of getting the global optimal over quickly finding a near-best solution to a given problem.…”

Section: Accuracy and Consistency Of The Parallel Acomentioning

confidence: 99%

“…Therefore, even though stated as optional, various local optimal operators have been incorporated with the basic ACO so as to further improve the performance of the ACOs [8]- [10]. However, the more sophisticated the local optimal operator, the slower the whole ACO process.…”

Section: Sequential Metaheuristic Aco Algorithm For Tspsmentioning

confidence: 99%

“…In fact, Eil51, Eil76 and KroA100 were selected because these cases are within the range that sequential ACO algorithms have performed best [8], [9].…”

Section: Sequential Metaheuristic Aco Algorithm For Tspsmentioning

confidence: 99%

“…Even more challenging in sequential ACO algorithms is the direct conflict between improving stagnation at the local optima and accelerating convergence. Avoiding local stagnation requires adding other disturbing operations into an ACO algorithm, but such an action inevitably prolongs the convergence [8], [9]. Given the multi-agent nature of ACOs, a few recent studies have explored the likelihood of efficiently applying parallel ACO algorithms to solve TSPs [14]- [19].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Dong¹,

Li²,

Shen³

et al. 2018

IJMLC

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Abstract-In recent years some comparative studies have explored the use of parallel ant colony optimization (ACO) algorithms over the traditionally sequential ACOs to solve the traveling salesman problem (TSP). However, these studies did not take a systematical approach to assess the performance of both algorithms on a comparable ground. In this paper, we aim to make a comparison of both the quality of the solutions and the running time as a result of the application of a sequential ACO and a parallel ACO to Eil51, Eil76 and KroA100 on a normalized and thus, comparable ground. Our study reaffirmed that the parallel algorithm is superior in computing efficiency over the sequential algorithm, particularly for larger TSPs. We also found that such a comparison could be meaningless if the size of the TSPs keeps increasing. We revealed that the worst solution among 10 repeated runs obtained from the parallel ACO was still better than the best solution among 10 repeated runs obtained from the sequential ACO, though both did not reach the global optimal solution within 300 iterations. The proposed parallel ACO has a very high consistency because at least one best solution was found within an error of 0.5% to the global optimal solution in every three repeats for all three cases.

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Section: Accuracy and Consistency Of The Parallel Acomentioning

confidence: 99%

Section: Sequential Metaheuristic Aco Algorithm For Tspsmentioning

confidence: 99%

“…In fact, Eil51, Eil76 and KroA100 were selected because these cases are within the range that sequential ACO algorithms have performed best [8], [9].…”

Section: Sequential Metaheuristic Aco Algorithm For Tspsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Dong¹,

Li²,

Shen³

et al. 2018

IJMLC

View full text Add to dashboard Cite

show abstract

“…To improve the convergence speed of the algorithm, G. Dong et al proposed a new hybrid algorithm, cooperative ant colony system and genetic algorithm (CoACSGA) [4]. In response to the economical operation of the inner-plant of a hydropower station, X. Wang et al proposed a new multicolony ant optimization (MCAO) combining with a dynamic economic distribution (DED) technique and established a patching mechanism [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Colony Ant Algorithm Using Both Generative Adversarial Nets and Adaptive Stagnation Avoidance Strategy

2020

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Aiming at Travel Salesman Problem (TSP) that ant colony algorithm is easy to fall into local optima and slow convergence, a multi-colony ant algorithm using both generative adversarial nets (GAN) and adaptive stagnation avoidance strategy (GAACO) is proposed. First, to improve the convergence speed of the algorithm, we introduce a GAN model based on the game between convergence speed and solution quality. Then, to overcome premature convergence, an adaptive stagnation avoidance strategy is proposed. The strategy consists of two parts: (1) information entropy. It is used to measure the diversity of GAACO; (2) a cooperative game model. When the value of information entropy is less than threshold value, the cooperative game model will be used to select the appropriate pheromone matrix for different colonies to improve the accuracy. Finally, to further accelerate the convergence of the algorithm, the initial pheromone matrix is preprocessed to increase the pheromone of the optimal path for each iteration in the early stage. And according to reinforcement learning method, each colony increases the pheromone of the global optimal path at the end of each iteration. Extensive experiments with numerous instances in the TSPLIB standard library show that the proposed methods significantly outperform the state-of-the-art multi-colony ant colony optimization algorithms, especially in the large-scale TSPs. INDEX TERMS Generative adversarial nets, cooperative game, information entropy, travel salesman problem.

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The hybridization of ACO + GA and RVNS algorithm for solving the time-dependent traveling salesman problem

Ban

2020

Evol. Intel.

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A Cooperative Ant Colony System and Genetic Algorithm for TSPs

Cited by 7 publications

References 19 publications

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Multi-Colony Ant Algorithm Using Both Generative Adversarial Nets and Adaptive Stagnation Avoidance Strategy

The hybridization of ACO + GA and RVNS algorithm for solving the time-dependent traveling salesman problem

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