A Performance Evaluation of Genetic Algorithm and Simulated Annealing for the Solution of TSP with Profit Using Python

Garg, Neha; Kakkar, Mohit Kumar; Gupta, Gourav; Singla, Jajji

doi:10.1007/978-981-19-4676-9_2

Cited by 3 publications

(1 citation statement)

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“…The authors in [47] propose an algorithm to minimize travel costs and maximize the overall profit. Simulated annealing (SA) and genetic algorithm (GA) with dedicated mutation operators were used.…”

Section: Related Workmentioning

confidence: 99%

IAM-TSP: Iterative Approximate Methods for Solving the Travelling Salesman Problem

Alkafaween,

Elmougy,

Essa

et al. 2023

IJACSA

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TSP is a well-known combinatorial optimization problem with several practical applications. It is an NP-hard problem, which means that the optimal solution for huge numbers of examples is computationally impractical. As a result, researchers have focused their efforts on devising efficient algorithms for obtaining approximate solutions to the TSP. This paper proposes Iterative Approximate Methods for Solving TSP (IAM-TSP), as a new method that provides an approximate solution to TSP in polynomial time. This proposed method begins by adding four extreme cities to the route, a loop, and then adds each city to the route using a greedy technique that evaluates the cost of adding each city to different positions along the route. This method determines the best position to add the city and the also the best city to be added. The resultant route is further improved by employing local constant permutations. When compared to existing state-of-the-art methods, our experimental results show that the proposed method is more capable of producing high-quality solutions. The proposed approach, with an average approximation of 1.09, can be recommended for practical usage in its current form or as a pre-processing step for another optimizer.

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Section: Related Workmentioning

confidence: 99%