A novel genetic algorithm to solve travelling salesman problem and blocking flow shop scheduling problem

Chowdhury, Arkabandhu; Ghosh, Arnab; Sinha, Subhajit; Das, Swagatam; Ghosh, Avishek

doi:10.1504/ijbic.2013.057172

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…This process starts from the edge that connects the latter with the first vertex (line 6-9). All weights of edges that connect every two consecutive remaining vertices are also added (lines [12][13][14]. The method for generating the initial population is GenerateRandomIndividuals (Fig.…”

Section: Materials and Algorithmmentioning

confidence: 99%

“…The computational complexity of this method is Θ(m.n 2 ), where n is the number of individuals in the population, and m is vertices in the graph. When changing the order of the individuals in the population (lines 5 and 7), all elements of the solution are also copied (lines [11][12][13]. After that, the scores of the solutions also exchanged (line 14).…”

Section: Materials and Algorithmmentioning

confidence: 99%

“…For an important class of NP-hard problems, such as scheduling problems and graph problems, good approximate solutions were found using GAs [11]- [13]. This class of problems are known for being similar but not identical to the problems for which there are efficient algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Kralev

2018

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents the results of an analysis of three algorithms for the Travelling Salesman Problem (TSP). The basic steps of genetic algorithms (GAs) and their benefits in solving combinatorial optimization problems are also presented. Moreover, several studies related to TPS and some approaches to its solution are discussed. An optimized version of the standard recursive algorithm for solving TSP using the backtracking method is presented. This algorithm is used to generate optimal solutions concerning the studied graphs. In addition, a standard genetic algorithm for solving TSP and its modification are also presented. The modified algorithm uses the genetic operator mutation in a different way. The results show that the recursive algorithm can be used successfully to solve the TSP for graphs with a small number of vertices, for instance, 25-30. The results of the two GAs were different. The modified GA found the optimal solutions for all tested graphs, while the standard GA found the optimal solutions in only 40% of the cases. These results were obtained for a reasonable time (in seconds), with appropriate values of the control parameters-population size and reproduction number. It appeared that the use of the genetic mutation operator yields better results when applied to identical solutions. If pairs of identical solutions are found in a population, then every second must mutate. The methodology and the conditions for conducting the experiments are described in details.

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Section: Materials and Algorithmmentioning

confidence: 99%

Section: Materials and Algorithmmentioning

confidence: 99%

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Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Kralev

2018

International Journal on Advanced Science, Engineering and Information Technology

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“…For the blocking constraint, few studies took into account the different criteria (Companys and Mateo, 2007;Ronconi, 2005;Moslehi and Khorasanian, 2013;Rippin, 1983;Dekhici and Belkadi, 2010). Other authors proposed an approximate methods (Ronconi, 2004;Armentano and Ronconi, 1999;Grabowski and Pempera, 2007;Wang et al, 2010;Companys et al, 2010;Chowdhury et al, 2013;Ribas et al, 2011). But few studies deal with the makespan criterion in the flowshop environment with blocking.…”

Section: Introductionmentioning

confidence: 99%

Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion

Toumi

Jarboui

Eddaly

et al. 2017

IJMOR

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This paper deals with a permutation flowshop scheduling problem with blocking constraints. To minimise the makespan, it uses a branch-and-bound algorithm. A new lower bound is developed for the problem based upon the two machine-subproblem relaxation. Computational tests show the superiority of the proposed algorithm.Keywords: scheduling; blocking flowshop; lower bound; makespan.Reference to this paper should be made as follows: Toumi, S., Jarboui, B., Eddaly M. and Rebaï, A. (2017) 'Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion', Int.

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“…For instance, Freisleben and Merz [9] presented an algorithm by using genetic algorithm (GA) to find near-optimal solution for a set of symmetric and asymmetric TSP instances and obtained high quality solutions in a reasonable time. Chowdhury et al [10] also used GA for solving a flow-shop scheduling problem to minimize makespan via finding optimal order of cities. The simulated annealing (SA) algorithm is also used for TSP by Wang and Tian [11] in which an improved SA is employed.…”

Section: Introductionmentioning

confidence: 99%

Using 2-Opt based evolution strategy for travelling salesman problem

Karagül¹,

Aydemir²,

Tokat³

2016

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Abstract. Harmony search algorithm that matches the (µ+1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions.

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A novel genetic algorithm to solve travelling salesman problem and blocking flow shop scheduling problem

Cited by 19 publications

References 38 publications

Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Different Applications of the Genetic Mutation Operator for Symetric Travelling Salesman Problem

Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion

Using 2-Opt based evolution strategy for travelling salesman problem

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