Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

Wang, Wenming; Zhao, Jiangdong; Huang, Jian

doi:10.1088/1742-6596/1693/1/012085

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…Since the objective function of the problem addressed in this paper is of min-max type, the grouping-based genetic algorithm exhibits better performance than a traditional genetic algorithm when solving this type of problem [ 17 ]. Inspired with the work of Singh and Baghel [ 18 ] and Han et al [ 30 ], Wang et al [ 31 ] proposed an improved grouping genetic algorithm and the associated genetic operators. Since the grouping genetic algorithm of Wang et al [ 31 ] was originally designed to solve the min–sum type MTSP, we have made appropriate modifications to solve the min-max type MTSP in this stage.…”

Section: Algorithm Designmentioning

confidence: 99%

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

Bao

Wang

et al. 2023

Biomimetics

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The min-max clustered traveling salesmen problem (MMCTSP) is a generalized variant of the classical traveling salesman problem (TSP). In this problem, the vertices of the graph are partitioned into a given number of clusters and we are asked to find a collection of tours to visit all the vertices with the constraint that the vertices of each cluster are visited consecutively. The objective of the problem is to minimize the weight of the maximum weight tour. For this problem, a two-stage solution method based on a genetic algorithm is designed according to the problem characteristics. The first stage is to determine the visiting order of the vertices within each cluster, by abstracting a TSP from the corresponding cluster and applying a genetic algorithm to solve it. The second stage is to determine the assignment of clusters to salesmen and the visiting order of the assigned clusters. In this stage, by representing each cluster as a node and using the result of the first stage and the ideas of greed and random, we define the distances between each two nodes and construct a multiple traveling salesmen problem (MTSP), and then apply a grouping-based genetic algorithm to solve it. Computational experiments indicate that the proposed algorithm can obtain better solution results for various scale instances and shows good solution performance.

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Section: Algorithm Designmentioning

confidence: 99%

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

Bao

Wang

et al. 2023

Biomimetics

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“…The optimization algorithm based on the ant colony principle [25] is a part of swarm intelligence algorithms and is a general-purpose optimization method. This method quite well solves the problem of finding routes on graphs and is used for the traveling salesman problem solving [26]. The method [25] proved to be good at solving both discrete and continuous optimization problems and problems with risks and limitations.…”

Section: The Problem Of Transport Logisticsmentioning

confidence: 99%

The Method of Transport Logistics Problem Solving by the MAX-MIN ACO Algorithm

Khudov¹,

Koval²,

Khizhnyak³

et al. 2022

IJETAE

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The article provides a detailed analysis of the well-known mathematical methods that are used in solving transport logistics problems of various types. Their advantages and disadvantages are determined. The article proposes to use the methods of swarm intelligence to find the optimal route, taking into account all forbidden zones. The problem of transport logistics was solved by the ant colony optimization algorithm on a control example. The results of work of the method were analyzed. The main disadvantages are established. It is proposed to use a modification of the ant colony algorithm - the Max-Min ant colony optimization algorithm. The effectiveness of the method for solving the problem of transport logistics by the algorithm of max-min optimization of an ant colony was tested on a control example. An experimental calculation of the optimal route of traffic from the starting point to the final point is given taking into account the forbidden zones.

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“…Ant colony optimization algorithm(ACO) is a swarm intelligence algorithm, which is widely studied TSP in recent years. The algorithm is also a biological simulation algorithm, which achieves the survival of the fittest by simulating the natural habits of organisms and the natural law of survival of the fittest Therefore, ant colony optimization algorithm is a heuristic algorithm, which compares and retains better results in the process of optimization through the positive feedback mechanism, so as to find the optimal solution [1][2][3]. However, in the process of optimization, the ant colony algorithm will fall into local optimization due to its own limitations, and cannot obtain the desired results.…”

Section: Introductionmentioning

confidence: 99%

“…In order to further optimize the ant colony algorithm model, Wenming Wang [1] et al proposed an improved ant colony genetic algorithm (IACGA) aiming at the problem of slow convergence speed and local optimum of the algorithm. This algorithm dynamically integrates the ant colony algorithm and genetic algorithm to effectively improve the efficiency of solving TSP problems.…”

Section: Introductionmentioning

confidence: 99%

Firefly algorithm and ant colony algorithm to optimize the traveling salesman problem

Zheng

et al. 2022

J. Phys.: Conf. Ser.

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Through the study of ACOTSP, it is found that the previous ant colony algorithm will fall into local optimal when increasing pheromone concentration factor. In order to solve the problem, we through the improved pheromone concentration factor to view your traveling salesman solving process, through the simulation experiments found that due to the pheromone concentration gradually increased with the number of iterations, pheromone concentration and pheromone concentration factor exponential relationship, lead to appear even if the distance is large move also can appear probability is very high. In this design, the ant colony algorithm is optimized by introducing firefly algorithm (FA): the ant colony movement deviation is avoided by adding disturbance factor; and the migration probability caused by excessive pheromone concentration is solved by adding function relation between moving distance and pheromone concentration. Simulation results show that the optimized algorithm has better results and is not easy to fall into local optimum.

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Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

Cited by 5 publications

References 4 publications

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

The Method of Transport Logistics Problem Solving by the MAX-MIN ACO Algorithm

Firefly algorithm and ant colony algorithm to optimize the traveling salesman problem

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