An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

Yousefikhoshbakht, Majid; Didehvar, Farzad; Rahmati, Farhad

doi:10.15837/ijccc.2014.3.161

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…T able 2 : summarizes the results obtained from the application of the proposed solution method, abbreviated as TSTS algorithm as the first method of the three strategies, on the problem instances of [5]. Furthermore, the detailed results of the best performing metaheuristic implementations from other authors are also provided, using the following abbreviations: SA and TS [18], genetic algorithm (GA) [2], scatter search algorithm combined by ACO (SS-ACO) [27], particle swarm intelligent (PSO) [1], genetic algorithm combined with particle swarm intelligent (GAPSO) [16] and (HEAS) [26] in addition to the Best Known Result (BKR). In addition to a multi-start version, where the algorithm is repeated 10 times and the best solution is kept.…”

Section: Computational Resultsmentioning

confidence: 99%

Applying Tabu Search in Finding an Efficient Solution for the OVRP

Hedar¹,

Abd-Allah²

2014

IJOPCSM

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In the open vehicle routing problem (OVRP), the target would be to reduce the amount of vehicles after reducing the whole distance (or time) travelled. Every route begins at the depot and comes to an end at a customer, going to many customers, every once, en route, without returning to the depot. The demand of every customer has to be completely fulfilled by a single vehicle. The total demand serviced by each vehicle has to not go over vehicle capacity. A highly effective tabu search for open vehicle routing called Three Strategies Tabu Search (TSTS) heuristic for this problem is suggested. The TSTS depends on three strategies MOVE, EXCHANGE and SWAP. Computational results on fourteen standard benchmark problem instances demonstrate that the suggested TSTS is comparable in terms of solution quality for the best performing published heuristics.

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Section: Computational Resultsmentioning

confidence: 99%

Applying Tabu Search in Finding an Efficient Solution for the OVRP

Hedar¹,

Abd-Allah²

2014

IJOPCSM

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“…So, these algorithms, despite taking more time to solve, are needed to produce better solutions than heuristic algorithms with appropriate solutions and make proper use of the concept of accident. Of course, it should be noted that the very good property of metaheuristic algorithms is that the time of implementation of the algorithm is user-dependent, and based on the acceptable time in each problem, the algorithm is implemented [17][18][19][20]. erefore, there is a direct relationship in these algorithms for the run time and the quality of the obtained solutions, which can change according to the user's diagnosis.…”

Section: The Related Work and The Classic Psomentioning

confidence: 99%

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Yousefikhoshbakht

2021

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The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used in many engineering sciences and has attracted the attention of many scientists and researchers. In this issue, a salesman starts to move from a desired node called warehouse and returns to the starting place after meeting n customers provided that each customer is only met once. The aim of this issue is to determine a cycle with a minimum cost for this salesman. One of the major weaknesses of the PSO algorithm in the classical version is that it gets stuck in local optimizations. Therefore, in the proposed algorithm, called MPSO, the best solution in the current iteration is also used in the movement step. In addition, a variety of local search algorithms are provided that are used when better answers are generated than before. Also, a new method for moving the particle towards the best particle is presented, which, in addition to probably increasing the quality of the new answer, prevents the premature convergence of the algorithm due to consideration of the concept of random. The results evaluated with the results of several metaheuristic algorithms in the literature show the efficiency of the MPSO algorithm because it has been able to achieve excellent solutions in most of these instances.

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“…This algorithm improves the performance of each individual of the population. We find other metaheuristic methods that have been developed for the resolution of the VRP who is one of the most famous combinatorial optimization problems [15], [21].…”

Section: Pso For the Vehicle Routing Problem And The Pick-up And Delimentioning

confidence: 99%

“…The chromosomes of the solution are encoded using path representation in which, for each depot, the couples are listed in the order in which they are visited [21].…”

Section: Structure Of the Initial Populationmentioning

confidence: 99%

A Comparative Study of the PSO and GA for the m-MDPDPTW

Alaïa

Dridi

Borne³

et al. 2018

INT J COMPUT COMMUN

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Abstract:The m-MDPDPTW is the multi-vehicles, multi-depots pick-up and delivery problem with time windows. It is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers for the purpose of satisfying precedence, capacity and time constraints. This problem is a very important class of operational research, which is part of the category of NP-hard problems. Its resolution therefore requires the use of evolutionary algorithms such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO). We present, in this sense, a comparative study between two approaches based respectively on the GA and the PSO for the optimization of m-MDPDPTW. We propose, in this paper, a literature review of the Vehicle Routing Problem (VRP) and the Pick-up and Delivery Problem with Time Windows (PDPTW), present our approaches, whose objective is to give a satisfying solution to the m-MDPDPTW minimizing the total distance travelled. The performance of both approaches is evaluated using various sets instances from [10] PDPTW benchmark data problems. From our study, in the case of m-MDPDPTW problem, the proposed GA reached to better results compared with the PSO algorithm and can be considered the most appropriate model to solve our m-MDPDPTW problem.

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An Efficient Solution for the VRP by Using a Hybrid Elite Ant System

Cited by 16 publications

References 19 publications

Applying Tabu Search in Finding an Efficient Solution for the OVRP

Applying Tabu Search in Finding an Efficient Solution for the OVRP

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

A Comparative Study of the PSO and GA for the m-MDPDPTW

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