Are Symmetric Traveling Salesman Problems Well Suited to Benchmark Some Quantum Optimization Problems?

2022 ◽  
Author(s):  
Richard H. Warren
Keyword(s):  
Optimization Problems ◽  
Traveling Salesman ◽  
Traveling Salesman Problems ◽  
Quantum Optimization

A benchmark for quantum optimization: the traveling salesman

2021 ◽  
Vol 21 (7&8) ◽  
pp. 557-562
Author(s):  
Richard H. Warren
Keyword(s):  
Quantum Computing ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Traveling Salesman Problems ◽  
Combinatorial Optimization Problems ◽  
Quantum Optimization

We present compelling reasons for symmetric traveling salesman problems (TSPs) to be the benchmark for quantum computing of combinatorial optimization problems for all types of quantum hardware. There are seven reasons for endorsing these TSPs to be the benchmark and no shortcomings.

Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems

2007 ◽  
Vol 11 (4) ◽  
pp. 433-442 ◽  
Author(s):  
Kuo-Sheng Hung ◽  
◽  
Shun-Feng Su ◽  
Zne-Jung Lee ◽  
◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Computational Time ◽  
Search Performance ◽  
Problem Size ◽  
Traveling Salesman Problems ◽  
Combinatorial Optimization Problems

Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this paper, we reported the analysis of using a lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From our experiments, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do.

K-means Clustering Algorithm and Meta-heuristics for Multiple Traveling Salesman Problems

2009 ◽  
Vol 4 (2) ◽  
pp. 16-32
Author(s):  
R. Nallusamy ◽  
K. Duraiswamy ◽  
R. Dhanalaksmi ◽  
P. Parthiban
Keyword(s):  
Clustering Algorithm ◽  
Traveling Salesman ◽  
Traveling Salesman Problems

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning

2021 ◽  
pp. 1-14
Author(s):  
Zizhen Zhang ◽  
Hong Liu ◽  
MengChu Zhou ◽  
Jiahai Wang
Keyword(s):  
Reinforcement Learning ◽  
Traveling Salesman ◽  
Traveling Salesman Problems

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Reducing Initial Edge Set of Traveling Salesman Problems

2007 ◽  
Author(s):  
Dong Wang ◽  
Xiang-Bin Wu ◽  
Xian-Cheng Mao ◽  
Wen-Jian Liu
Keyword(s):  
Traveling Salesman ◽  
Traveling Salesman Problems
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