A New Simulated Annealing Approach for Travelling Salesman Problem

Bayram, Hüsamettin; Şahin, Ramazan

doi:10.3390/mca18030313

Cited by 12 publications

(2 citation statements)

References 20 publications

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“…In [40], Alper et al applied the SA algorithm to solve the graph coloring problem. In [31,43], Delahaye et al implemented the SA algorithm to solve two NPhard combinatorial optimization problems: the traveling salesman problem and the knapsack problem. SA has been implemented as well to solve continuous optimization problems.…”

Section: Advantages Limitations and Applications Of Ss-based Algorithmsmentioning

confidence: 99%

Review on Nature-Inspired Algorithms

Korani

Mouhoub

2021

SN Oper. Res. Forum

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Section: Advantages Limitations and Applications Of Ss-based Algorithmsmentioning

confidence: 99%

Review on Nature-Inspired Algorithms

Korani

Mouhoub

2021

SN Oper. Res. Forum

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“…• Airline Crew Scheduling [8] • Railway Crew Scheduling [9] • Traveling Salesman Problem [4] • Vehicle Routing Problem [14] • Layout-Routing of Electronic Circuits [17] • Large Scale Aircraft Trajectory Planing [5,10] • Complex portfolio problem [7] • Graph coloring problem [6] • High-dimensionality minimization problems [16] Figure 11: Final tour of the TSP with n = 1, 000 cities.…”

Section: Simulated Annealing Implementationmentioning

confidence: 99%

Simulated Annealing: From Basics to Applications

Delahaye

Chaimatanan

Mongeau

2018

International Series in Operations Research &Amp; Management Science

180

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Simulated Annealing (SA) is one of the simplest and best-known metaheuristic method for addressing the difficult black box global optimization problems (those whose objective function is not explicitly given and can only be evaluated via some costly computer simulation). It is massively used on real-life applications. The main advantage of SA is its simplicity. SA is based on an analogy with the physical annealing of materials that avoids the drawback of the Monte-Carlo approach (which can be trapped in local minima), thanks to an efficient Metropolis acceptance criterion. When the evaluation of the objective-function results from complex simulation processes manipulating a large-dimension state space involving much memory, population-based algorithms are not applicable and SA is the right answer to address such issues. This chapter is an introduction to the subject. It presents the principles of local search optimization algorithms, of which simulated annealing is an extension, and the Metropolis algorithm, a basic component of SA. The basic SA algorithm for optimization is described together with two theoretical aspects that are fundamental to SA: statistical equilibrium (inspired from elementary statistical physics) and asymptotic convergence (based on Markov chain theory). The chapter surveys the following practical issues of interest to the user that wishes to implement the SA algorithm for its particular application: finite-time approximation of the theoretical SA, polynomial-time cooling, Markov-chain length, stopping criteria, and simulated-based evaluations. To illustrate these concepts, this chapter presents the straightforward application of SA to two classical and simple 1 classical NP-hard combinatorial optimization problems: the knapsack problem and the traveling salesman problem. The overall SA methodology is then deployed in detail on a real-life application: a large-scale aircraft trajectory planning problem involving nearly 30,000 flights at the European continental scale. This exemplifies how to tackle nowadays complex problems using the simple scheme of SA by exploiting particular features of the problem, by integrating astute computer implementation within the algorithm, and by setting user-defined parameters empirically, inspired by the SA basic theory presented in this chapter.

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An Approximate Solution of the Travelling Salesman Problem Based on the Metropolis Simulation with Annealing

Товстик

2014

Springer Proceedings in Mathematics &Amp; Statistics

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A New Simulated Annealing Approach for Travelling Salesman Problem

Cited by 12 publications

References 20 publications

Review on Nature-Inspired Algorithms

Review on Nature-Inspired Algorithms

Simulated Annealing: From Basics to Applications

An Approximate Solution of the Travelling Salesman Problem Based on the Metropolis Simulation with Annealing

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