A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Mahi, Mostafa; Baykan, Ömer Kaan; Kodaz, Halife

doi:10.1016/j.asoc.2015.01.068

Cited by 341 publications

(158 citation statements)

References 20 publications

Supporting

Mentioning

158

Contrasting

Order By: Relevance

“…In the second experiment, the proposed algorithm was compared with four state-of-the-art metaheuristic algorithms: Chen and Chien [27], Wang et al [41], Yousefikhoshbakht et al [32], and Mahi et al [31]. The authors of these algorithms proved that their algorithms outperformed the other algorithms in the literature.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Gündüz et al [30] presented a hybrid ACO with ABC for solving TSP. In addition, Mahi et al [31] proposed a new algorithm in which ACO was hybridized with PSO and 3-Opt for solving small TSP instances. The PSO was used to determine the optimum values of the two main parameters of ACO which affected algorithm performance and the 3-Opt was used to escape from the local optima found by ACO algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

Mohsen

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

Mohsen

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…We can also find a lot of publications concerning routes optimisation using ACO theory to find optimal path in a graph 7,8 , or to solve the travelling salesman problem 9,10 , or the vehicle routing problem 11,12 .…”

Section: Literature Reviewmentioning

confidence: 99%

Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

Stodola¹,

Mazal²

2017

Def. Sc. Jl.

View full text Add to dashboard Cite

The model of optimal cooperative reconnaissance as a part of the tactical decision support system to aid commanders in their decision-making processes is presented. The model represents one of the models of military tactics implemented in the system to plan the ground reconnaissance operation for the commander optimally. The main goal of the model is to explore the area of interest by multiple military elements (scouts, UAVs, UGVs) as quickly as possible. A metaheuristic solution to this problem which combines two probabilistic methods: simulated annealing and the ant colony optimisation algorithm is proposed. In the first part of this study, the optimal cooperative reconnaissance problem is formulated. Then, metaheuristic solution, which is composed of three independent steps, is presented. Finally, experiments are conducted to verify the approach to this problem.

show abstract

“…Junqiang Wu [5] proposed a hybrid ACO with beam search for solving TSP. Mahi [6] proposed a new algorithm in which ACO was hybridized with PSO and 3-opt for solving small instances.…”

Section: Related Workmentioning

confidence: 99%

A Randomness Ant Colony Algorithm for Solving TSP

Niu¹,

Zhang²

2017

dtcse

View full text Add to dashboard Cite

Abstract. The Travelling Salesman Problem (TSP) has received much attention because of its practical applications in several problems. One of the commonly used algorithms to solve TSP is Ant Colony Optimization (ACO). In this paper, a randomness ant colony algorithm is proposed to solve the defects of ACO by optimizing the path selection probability update rule and pheromone update rule. As the improved algorithm is applied to solve the classical TSP problem, the results show that it is more effective and converge faster.

show abstract

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Cited by 341 publications

References 20 publications

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

A Randomness Ant Colony Algorithm for Solving TSP

Contact Info

Product

Resources

About