A Boolean neural network approach for the traveling salesman problem

Bhide, S.; John, Nigel; Kabuka, Mansur R.

doi:10.1109/12.257714

Cited by 32 publications

(14 citation statements)

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“…branch and-bound [12], neural network [13] or Tabu search [14]. Some of these methods are exact algorithms, while others are near-optimal or approximate algorithms.…”

Section: Related Workmentioning

confidence: 99%

GELS-GA: Hybrid metaheuristic algorithm for solving Multiple Travelling Salesman Problem

Hosseinabadi

Kardgar

Shojafar

et al. 2014

2014 14th International Conference on Intelligent Systems Design and Applications

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The Multiple Traveling Salesmen Problem (mTSP) is of the famous and classical problems of research in operations and is accounted as one of the most famous and widely used problems of combinational optimization. Most of the complex problems can be modeled as the mTSP and then be solved. The mTSP is a NP-Complete one; therefore, it is not possible to use the exact algorithms for solving it instead the heuristics methods are often applied for solving such problems.In this paper, a new hybrid algorithm, called GELS-GA, has been presented for solving the mTSP. The utility of GELS-GA is compared with some related works such as GA and ACO and achieves optimality even in highly complex scenarios.Although, the proposed algorithm is simple, it includes an appropriate time of completion and the least traversed distance among existing algorithms.

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“…branch and-bound [12], neural network [13] or Tabu search [14]. Some of these methods are exact algorithms, while others are near-optimal or approximate algorithms.…”

Section: Related Workmentioning

confidence: 99%

GELS-GA: Hybrid metaheuristic algorithm for solving Multiple Travelling Salesman Problem

Hosseinabadi

Kardgar

Shojafar

et al. 2014

2014 14th International Conference on Intelligent Systems Design and Applications

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“…branchand-bound [9], cutting planes [18], neural network [5] or tabu search [12]. Some of these methods are exact algorithms, while others are near-optimal or approximate algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Multiple Traveling Salesmen Problem by a Novel Representation Based Genetic Algorithm

Király

Abonyi

2011

Intelligent Computational Optimization in Engineering

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The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several costumer demands; find the set of routes with overall minimum route cost which service all the demands. The multiple traveling salesman problem (mTSP) is a generalization of the well-known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. It is well-known that mTSP-based algorithms can also be utilized in several VRPs by incorporating some additional site constraints. The aim of this chapter is to review how genetic algorithms can be applied to solve these problems and to propose a novel, interpretable representation based algorithm.The elaborated heuristic algorithm is demonstrated by examples considering different round tour types determination of further tasks for optimal operation of the distribution system for instance the modification of the vehicle capacity, and the effects of change of cost elements and data structure.

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“…The TSP combinatorial complexity makes difficult to achieve a methodology to solve efficiently TSP of realistic size to global optimality. A number of different methods have been proposed for obtaining either optimal or near optimal solutions, ranging from classic methodologies based on linear programming (Wong, 1980) and branch-and-bound (Little et al, 1963;Bellmore & Malone, 1971) to artificial intelligence methods such as neural networks (Bhide et al, 1993), tabu search (Glover, 1990), and genetic algorithms (Goldberg & Lingle, 1985). SpIteR does not uses only one method, but here some aspects of genetic one are shortly presented.…”

Section: Spitermentioning

confidence: 99%