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.

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.

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.

scholarly journals A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chun-Wei Tsai ◽  
Shih-Pang Tseng ◽  
Ming-Chao Chiang ◽  
Chu-Sing Yang ◽  
Tzung-Pei Hong
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
High Performance ◽  
Computation Time ◽  
Traveling Salesman ◽  
Final Solution ◽  
Extensive Analysis ◽  
The Traveling Salesman Problem ◽  
The Impact

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

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 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 Solving the Traveling Salesman Problem with Ant Colony Optimization: A Revisit and New Efficient Algorithms

2013 ◽  
Vol 2 (3-4) ◽  
Author(s):  
Hoang Xuan Huan ◽  
Nguyen Linh-Trung ◽  
Do Duc Dong ◽  
Huu-Tue Huynh
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant System ◽  
Benchmark Datasets ◽  
General Aspects ◽  
The Traveling Salesman Problem ◽  
Combinatoric Optimization

Ant colony optimization (ACO) techniques are known to be efficient for combinatorial optimization. The traveling salesman problem (TSP) is the benchmark used for testing new combinatoric optimization algorithms. This paper revisits the application of ACO techniques to the TSP and discuss some general aspects of ACO that have been previously overlooked. In fact, it is observed that the solution length does not reflect exactly the quality of a particular edge belong to the solution, but it is only used for relatively evaluating whether the edge is good or bad in the process of reinforcement learning. Based on this observation, we propose two algorithms– Smoothed Max-Min Ant System and Three-Level Ant System– which not only can be easily implemented but also provide better performance, as compared to the well-known Max-Min Ant System. The performance is evaluated by numerical simulation using benchmark datasets.

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.

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