The Design and Analysis of TSP Problem Based on Genetic Algorithm and Ant Colony Algorithm

Bi, Shuoben; Dong, Xianguang; Ma, Yan

doi:10.5815/ijeme.2012.09.09

Cited by 9 publications

(9 citation statements)

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“…The principle of GA is based on the processes occurring in living nature due to evolution and reproduces Ch. Darwin's idea of natural selection (Bi, Dong, & Ma, 2012). GA is used for solving optimization problems by random selection, combination and variation of the desired parameters.…”

Section: Application Of Genetic Algorithm To Solve the Travelling Salmentioning

confidence: 99%

“…The problem statement and several algorithms of its solution are presented in it. Nowadays the solution of this problem has rather a theoretical value for the scientists than a practical one, as it serves as the basis for developing new optimization algorithms, for instance, (Bi, Dong, & Ma, 2012). Still, the practical side of the TSP is even more interesting as it has a large number of applications from direct allocation, incorporated in the routing task (Kouki, Chaar, & Ksouri, 2009) to DNA problems (Zhong, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Solution of Travelling Salesman Problem for Wholesale Outlays Considering Repeated Visitation of Certain Sales Outlets

Matsiuk¹

2015

MEST Journal

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This paper presents the solving of the travelling salesman problem with repeated visitation of several outlets. The genetic algorithm was used as a technique for the finding the shortest route in the case of visiting every destination point. The three cases are described in the present research: the solving of the Euclidean travelling salesman problem, the solving of the travelling salesman problem considering the region transportation network and the solving of the travelling salesman problem with repeated visitation of some sales outlets. The solving algorithm which involves partitioning the route into sections without repeated visits to sales outlets has been proposed. Obtained results were combined into one which was considered as optimized for a given route. As a result of our investigation we have proposed the optimized routes that are up to 29% shorter than the actual ones used by the wholesale trade enterprises. As we can see this methodology provides a significant reduction of the transportation expenses of such organizations.

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Section: Application Of Genetic Algorithm To Solve the Travelling Salmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solution of Travelling Salesman Problem for Wholesale Outlays Considering Repeated Visitation of Certain Sales Outlets

Matsiuk¹

2015

MEST Journal

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“…15 In general, when the amount of ants and the amount of picking points are close or the same (between 5 and 15), the ACO is better than the GA in searching optimized solution of traveling salesman problem (TSP). 16 When the numbers of picking points are between 0 and 20, the ACO is more suitable than any other algorithm in searching optimized solution.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization model with characterization-based speed and multi-driver for the refilling system in hospital

Jin

Wang

Cai

et al. 2017

Advances in Mechanical Engineering

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Different optimization algorithms are used in fast medicine dispensing system to improve the efficiency of refilling. To achieve rapid replenishing of the manipulator and improve efficiency in pharmacy automation system. An ant colony optimization algorithm, which is named ant colony optimization model with characterization-based speed and multidriver, is proposed. Simulations of different picking points by order-picking mode, particle swarm optimization, particle swarm optimization with characterization-based speed and multi-driver, genetic algorithm, genetic algorithm with characterization-based speed and multi-driver, ant colony optimization, and ant colony optimization with characterization-based speed and multi-driver were carried out, and practical tests of different picking points by order-picking mode and ant colony optimization with characterization-based speed and multi-driver were done in a hospital. The data were collected and comparisons were made. Comparisons among order-picking mode, particle swarm optimization, genetic algorithm, particle swarm optimization with characterization-based speed and multi-driver, genetic algorithm with characterization-based speed and multi-driver, ant colony optimization, and ant colony optimization with characterization-based speed and multi-driver are made. Practical test results in hospital show that the ranges of optimization ratios of the average time values tested by ant colony optimization with characterization-based speed and multi-driver and the order-picking mode are 12.4%-40.3% when the picking points are between 5 and 20. Ant colony optimization with characterization-based speed and multi-driver is suitable for the refilling process in fast medicine dispensing system. It is capable of finding the refilling route which spends the shortest refilling time, and this meets the requirements in hospital.

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“…Coding uses natural number coding, each natural number is the number of one city that represents a gene on chromosome. Suppose there are n cities, and then there are n genes on chromosome, so the sequence number of genes represents a path, such as chromosome (3,5,2,8,6,1,4,7,3) represents that you start from the city 3, and pass through the city 5, 2, 8, 6, 1, 4, 7, and finally you must back to the initial city.…”

Section: Genetic Algorithm For Solving Tsp Problemmentioning

confidence: 99%

“…Genetic algorithm regards all individuals in a population as objects and uses randomization technique to guide an efficient search for the coding parameter space [6]. Work and basic tasks it must complete consist genetic coding, fitness function, genetic operator, algorithm' parameter and termination condition of algorithm.…”

Section: Genetic Algorithm For Solving Tsp Problemmentioning

confidence: 99%

Genetic Algorithm for TSP Problem

Sun¹

2015

Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

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Abstract. The TSP is a typical NP problem. Therefore it is very important to the optimization for solving TSP problem. The genetic algorithm is one of ideal methods in solving it. However, the traditional genetic algorithm has the problems of slow convergence speed and local optimum. Aiming at the existing problems, the paper improved various operations and operators of traditional genetic algorithm, so that a better solution was put forward to solve the problem of local optimum prematurely. After making the selection operation, the elitist strategy was introduced to ensure the quality of the population gene; using adaptive mutation operator in mutation operation to improve the mutation quality and search result of algorithm. Genetic algorithm has characteristics of accurate result and fast convergence in solving TSP problem.

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The Design and Analysis of TSP Problem Based on Genetic Algorithm and Ant Colony Algorithm

Cited by 9 publications

References 0 publications

Solution of Travelling Salesman Problem for Wholesale Outlays Considering Repeated Visitation of Certain Sales Outlets

Solution of Travelling Salesman Problem for Wholesale Outlays Considering Repeated Visitation of Certain Sales Outlets

Ant colony optimization model with characterization-based speed and multi-driver for the refilling system in hospital

Genetic Algorithm for TSP Problem

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