An Investigation of Hybrid Tabu Search for the Traveling Salesman Problem

Xu, Dan; Weise, Thomas; Wu, Yuezhong; Lässig, Jörg; Chiong, Raymond

doi:10.1007/978-3-662-49014-3_47

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mutation can be a substantial operation for other heuristic algorithms as well e.g., simulated annealing [36], variable neighborhood search [37], tabu search [38] and Hill climbing [39]. Locus mutation works well with genetic algorithm solving TSP problem giving better results than the baseline, as we illustrated in earlier paragraphs.…”

Section: Using Locus Mutation With Other Heuristic Algorithmsmentioning

confidence: 80%

Adaptive Gene Level Mutation

Al-Afandi

András

2021

Algorithms

View full text Add to dashboard Cite

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

show abstract

Section: Using Locus Mutation With Other Heuristic Algorithmsmentioning

confidence: 80%

Adaptive Gene Level Mutation

Al-Afandi

András

2021

Algorithms

View full text Add to dashboard Cite

show abstract

“…Results indicated that the proposed algorithm in this paper could obtain the best solutions for most TSPLIB benchmarks. Xu et al [13] proposed a new tabu-search algorithm based on the evolutionary and ant-colony algorithms to effectively solve the traveling-salesman problem. Archetti et al [14] proposed an integer linear programming model based on the tabu-search algorithm to solve the vehicle-routing problem.…”

Section: Related Workmentioning

confidence: 99%

“…To solve the problem, besides traditional backtracking algorithms, branch-and-bound algorithms, and greedy algorithms, we mainly use heuristic search algorithms to optimize. These heuristic algorithms include the simulated annealing algorithm [8][9][10][11][12], tabu-search algorithm [13][14][15], ant-colony optimization algorithm [16][17][18], and genetic algorithm [19][20][21][22][23][24]. However, the optimization time of the simulated annealing algorithm is very long, the tabu-search algorithm has a strong dependence on the initial solution and can only be serialized, genetic algorithm is easy to fall into local optimization and has poor convergence performance, and the ant-colony algorithm also has long optimization time and is also prone to falling into local optimization [25].…”

Section: Introductionmentioning

confidence: 99%

Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming

Zhou

Zhu

et al. 2018

Information

View full text Add to dashboard Cite

The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.

show abstract

Global versus local search: the impact of population sizes on evolutionary algorithm performance

Weise

Chiong

et al. 2016

J Glob Optim

View full text Add to dashboard Cite

An Investigation of Hybrid Tabu Search for the Traveling Salesman Problem

Cited by 8 publications

References 21 publications

Adaptive Gene Level Mutation

Adaptive Gene Level Mutation

Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming

Global versus local search: the impact of population sizes on evolutionary algorithm performance

Contact Info

Product

Resources

About