Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem

1989 ◽  
Vol 21 (1) ◽  
pp. 59-84 ◽  
Author(s):  
Miroslaw Malek ◽  
Mohan Guruswamy ◽  
Mihir Pandya ◽  
Howard Owens
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Search Algorithms ◽  
Traveling Salesman ◽  
Parallel Simulated Annealing ◽  
The Traveling Salesman Problem

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

2019 ◽  
Vol 9 (19) ◽  
pp. 3985
Author(s):  
Lahari Sengupta ◽  
Radu Mariescu-Istodor ◽  
Pasi Fränti
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Processing Time ◽  
Closed Loop ◽  
Open Loop ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem ◽  
Local Search Operator

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.

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

Information ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 7 ◽  
Author(s):  
Ai-Hua Zhou ◽  
Li-Peng Zhu ◽  
Bin Hu ◽  
Song Deng ◽  
Yan Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Heuristic Algorithms ◽  
Gene Expression Programming ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Np Hard Problem ◽  
Convergence Performance ◽  
The Traveling Salesman Problem

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.

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