A Performance Comparison of Genetic Algorithm’s Mutation Operators in n-Cities Open Loop Travelling Salesman Problem

Chieng, Hock Hung; Wahid, Noorhaniza

doi:10.1007/978-3-319-07692-8_9

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…Iterations and repetitions are the most significant parameters of the proposed method. Additionally, a suitable number of iterations and repetitions are essential with respect to the problem size, which is 2 13 and 2 5 for the O-Mopsi dataset, and 2 15 and 2 6 for the Dots dataset. For O-Mopsi instances, processing times are 0.8 ms (single) and 16 ms (repeats).…”

Section: Discussionmentioning

confidence: 99%

“…In this context, we want to mention, as there are not a sufficient number of samples with more than 25 targets in the O-Mopsi dataset and 30 targets in the Dots dataset, we ignore those samples. From the chart, we can conclude that proper parameter values to use for the O-Mopsi dataset are I = 2 13 and R = 2 5 . The dots dataset requires higher values of I = 2 15 and R = 2 6 .…”

Section: Parameter Valuesmentioning

confidence: 99%

“…This corresponds to a special case of the orienteering problem [4]. Finding the optimum order by minimizing the tour length corresponds to solving open-loop TSP [5], because the players are not required to return to the start position. The open-loop variant is also NP-hard [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Which Local Search Operator Works Best for the Open-Loop TSP?

2019

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The traveling salesman problem (TSP) has been widely studied for the classical closed-loop variant. However, very little attention has been paid to the open-loop variant. Most of the existing studies also focus merely on presenting the overall optimization results (gap) or focus on processing time, but do not reveal much about which operators are more efficient to achieve the result. In this paper, we present two new operators (link swap and 3–permute) and study their efficiency against existing operators, both analytically and experimentally. Results show that while 2-opt and relocate contribute equally in the closed-loop case, the situation changes dramatically in the open-loop case where the new operator, link swap, dominates the search; it contributes by 50% to all improvements, while 2-opt and relocate have a 25% share each. The results are also generalized to tabu search and simulated annealing.

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Section: Discussionmentioning

confidence: 99%

Section: Parameter Valuesmentioning

confidence: 99%

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Which Local Search Operator Works Best for the Open-Loop TSP?

2019

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“…Sometimes, this technique is also called interchange mutation, or exchange mutation or random swap. For example, consider the tour represented by [25]:…”

Section: Improved Particle Swarm Optimization (Ipso)mentioning

confidence: 99%

Solving Travelling Salesman Problem by Using Optimization Algorithms

Saud

Kodaz

Babaoğlu

2018

KSS

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This paper presents the performances of different types of optimization techniques used in artificial intelligence (AI), these are Ant Colony Optimization (ACO), Improved Particle Swarm Optimization with a new operator (IPSO), Shuffled Frog Leaping Algorithms (SFLA) and modified shuffled frog leaping algorithm by using a crossover and mutation operators. They were used to solve the traveling salesman problem (TSP) which is one of the popular and classical route planning problems of research and it is considered as one of the widely known of combinatorial optimization. Combinatorial optimization problems are usually simple to state but very difficult to solve. ACO, PSO, and SFLA are intelligent meta-heuristic optimization algorithms with strong ability to analyze the optimization problems and find the optimal solution. They were tested on benchmark problems from TSPLIB and the test results were compared with each other.Keywords: Ant colony optimization, shuffled frog leaping algorithms, travelling salesman problem, improved particle swarm optimization

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“…The effectiveness of combining permutation coding with the evolution optimization of the TSP was noted. In the work [22], the mutation operators acting on permutations were compared. The conclusions point to the inversion, displacement, and pairwise swap methods as computationally fast solutions.…”

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confidence: 99%

Effective Permutation Encoding for Evolutionary Optimization of the Electric Vehicle Routing Problem

Iwańkowicz¹

2021

Energies

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This paper addresses the problem of route planning for a fleet of electric vehicles departing from a depot and supplying customers with certain goods. This paper aims to present a permutation-based method of vehicle route coding adapted to the specificity of electric drive. The developed method integrated with an evolutionary algorithm allows for rapid generation of routes for multiple vehicles taking into account the necessity of supplying energy in available charging stations. The minimization of the route distance travelled by all vehicles was taken as a criterion. The performed testing indicated satisfactory computation speed. A real region with four charging stations and 33 customers was analysed. Different scenarios of demand were analysed, and factors affecting the results of the proposed calculation method were indicated. The limitations of the method were pointed out, mainly caused by assumptions that simplify the problem. In the future, it is planned for research and method development to include the lapse of time and for the set of factors influencing energy consumption by a moving vehicle to be extended.

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A Performance Comparison of Genetic Algorithm’s Mutation Operators in n-Cities Open Loop Travelling Salesman Problem

Cited by 15 publications

References 12 publications

Which Local Search Operator Works Best for the Open-Loop TSP?

Which Local Search Operator Works Best for the Open-Loop TSP?

Solving Travelling Salesman Problem by Using Optimization Algorithms

Effective Permutation Encoding for Evolutionary Optimization of the Electric Vehicle Routing Problem

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