scholarly journals An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Pengzhen Du ◽  
Ning Liu ◽  
Haofeng Zhang ◽  
Jianfeng Lu
Keyword(s):  
Traveling Salesman Problem ◽  
Large Scale ◽  
Traveling Salesman ◽  
Local Optimum ◽  
Solution Quality ◽  
Movement Strategies ◽  
Comparison Algorithms ◽  
Adaptive Heuristic ◽  
The Traveling Salesman Problem ◽  
Local Optimizer

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms.

scholarly journals Heuristic Approaches to Solve Traveling Salesman Problem

2015 ◽  
Vol 15 (2) ◽  
pp. 390
Author(s):  
Malik Muneeb Abid ◽  
Iqbal Muhammad
Keyword(s):  
Exact Solution ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Solution Quality ◽  
Heuristic Approaches ◽  
The Traveling Salesman Problem

This paper provides the survey of the heuristics solution approaches for the traveling salesman problem (TSP). TSP is easy to understand, however, it is very difficult to solve. Due to complexity involved with exact solution approaches it is hard to solve TSP within feasible time. That’s why different heuristics are generally applied to solve TSP. Heuristics to solve TSP are presented here with detailed algorithms. At the end, comparison among selected approaches shows the efficiency of approaches in terms of solution quality and time consumed to solve TSP.

scholarly journals ISLAND MIGRATION MODEL WITH PARALLEL MUTATION STRATEGIES FOR COMPUTING THE TRAVELING SALESMAN PROBLEM ON MULTICOMPUTER PLATFORM

2014 ◽  
pp. 96-102
Author(s):  
Plamenka I. Borovska ◽  
Subhi A. Bahudaila ◽  
Milena K. Lazarova
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Coarse Grained ◽  
Mutation Rates ◽  
Solution Quality ◽  
Suggested Approach ◽  
Mutation Strategy ◽  
The Traveling Salesman Problem ◽  
The Impact

This paper investigates the efficiency of a model of parallel genetic computation of the traveling salesman problem with circular periodic chromosomes migration. The parallel model is verified by MPI-based program implementation on a multicomputer platform. The correlation of the application and architectural spaces has been investigated by exploring the impact of the scalability of the application and the parallel machine size over the efficiency of the parallel system. Performance profiling, evaluation and analysis have been made for different numbers of cities and different sizes of the multicomputer platform. The paper also investigates the impact of the mutation strategy on the solution quality of coarse-grained parallel genetic algorithm with circular periodic migration for the traveling salesman problem. We propose an approach to improve the quality of solution by applying parallel variable mutation rates for the local evolutions in the concurrent processes. A series of experiments has been carried out with parallel fixed and variable mutation rates in order to estimate the efficiency of the suggested approach. The best quality solutions have been obtained for the strategy with parallel fixed mutation rates.

Local Clustering Organization (LCO) Solving a Large-Scale TSP

2005 ◽  
Vol 17 (5) ◽  
pp. 560-567 ◽  
Author(s):  
Masashi Furukawa ◽  
◽  
Michiko Watanabe ◽  
Yusuke Matsumura ◽  
◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Numerical Experiments ◽  
Large Scale ◽  
The Self ◽  
Traveling Salesman ◽  
Self Organizing Map ◽  
Two Cities ◽  
Local Clustering ◽  
Different Types ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.

scholarly journals Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ho-Yoeng Yun ◽  
Suk-Jae Jeong ◽  
Kyung-Sup Kim
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Harmony Search ◽  
Solution Space ◽  
Global Optimum ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Optimum Solution ◽  
The Traveling Salesman Problem

We propose a novel heuristic algorithm based on the methods of advanced Harmony Search and Ant Colony Optimization (AHS-ACO) to effectively solve the Traveling Salesman Problem (TSP). The TSP, in general, is well known as an NP-complete problem, whose computational complexity increases exponentially by increasing the number of cities. In our algorithm, Ant Colony Optimization (ACO) is used to search the local optimum in the solution space, followed by the use of the Harmony Search to escape the local optimum determined by the ACO and to move towards a global optimum. Experiments were performed to validate the efficiency of our algorithm through a comparison with other algorithms and the optimum solutions presented in the TSPLIB. The results indicate that our algorithm is capable of generating the optimum solution for most instances in the TSPLIB; moreover, our algorithm found better solutions in two cases (kroB100 and pr144) when compared with the optimum solution presented in the TSPLIB.

New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem

2016 ◽  
pp. 1739-1752 ◽  
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Methods ◽  
Search Space ◽  
Traveling Salesman ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Hard Problems ◽  
The Traveling Salesman Problem ◽  
Np Hard Problems

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

scholarly journals Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1837 ◽  
Author(s):  
Dahan ◽  
El Hindi ◽  
Mathkour ◽  
AlSalman
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Service Selection ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighboring Node ◽  
Solution Quality ◽  
Web Service Selection ◽  
The Traveling Salesman Problem

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time.

scholarly journals Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

1993 ◽  
Vol 1 (4) ◽  
pp. 313-333 ◽  
Author(s):  
Christine L. Valenzuela ◽  
Antonia J. Jones
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Search Problem ◽  
Genetic Approach ◽  
Solution Quality ◽  
Alternative Approach ◽  
The Traveling Salesman Problem

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem

2015 ◽  
Vol 6 (2) ◽  
pp. 33-44 ◽  
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Methods ◽  
Search Space ◽  
Traveling Salesman ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Solution Quality ◽  
Hard Problems ◽  
The Traveling Salesman Problem

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

scholarly journals A Multi Ant Colony Optimization Approach For The Traveling Salesman Problem

2021 ◽  
Vol Volume 34 - 2020 - Special... ◽  
Author(s):  
Mathurin SOH ◽  
Baudoin Nguimeya Tsofack ◽  
Clémentin Tayou Djamegni
Keyword(s):  
Traveling Salesman Problem ◽  
Execution Time ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
Optimization Approach ◽  
Solution Quality ◽  
New Approach ◽  
International Audience ◽  
The Traveling Salesman Problem

International audience In this paper, we propose a new approach to solving the Traveling Salesman Problem (TSP), for which no exact algorithm is known that allows to find a solution in polynomial time. The proposed approach is based on optimization by ants. It puts several colonies in competition for improved solutions (in execution time and solution quality) to large TSP instances, and allows to efficiently explore the range of possible solutions. The results of our experiments show that the approach leads to better results compared to other heuristics from the literature, especially in terms of the quality of solutions obtained and execution time.

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