Performance enhancement in solving Traveling Salesman Problem using hybrid genetic algorithm

Kaur, Devinder; Murugappan, M.

doi:10.1109/nafips.2008.4531202

Cited by 28 publications

(15 citation statements)

References 2 publications

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“…The NN approach used here is similar to that of [49][50][51][52][53], but not exactly the same, as we implemented the general NN approach and compared with our own implementation rather than comparing with each specific NN method. The general NN approach used for our comparison is based on selecting a current city randomly, then linking the nearest city to the current city and carry on until linking all cities to form the final rout.…”

Section: Resultsmentioning

confidence: 99%

“…NN may still good alternative to random initial population technique in order to generate an initial population solutions that are used in solving TSP with GAs [49][50][51][52][53]. In the case of NN technique, the generation of each individual starts by selecting random city as the starting city and then the nearest city to be added as the new starting city.…”

Section: Population Seeding Techniquesmentioning

confidence: 99%

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An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

et al. 2018

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Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence.

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Section: Resultsmentioning

confidence: 99%

Section: Population Seeding Techniquesmentioning

confidence: 99%

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

et al. 2018

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“…The location of the WFA's one-water flow (a single solution agent) is assigned based on the initial solution value. In the WFA-TSP, the initial solution is generated using a greedy constructive method known as the NN heuristic, as used in [23], to generate a close approximate solution for the problem. Table 1 describes the parameters of the WFA-TSP.…”

Section: Proposed Water Flow-likementioning

confidence: 99%

A Water Flow-Like Algorithm for the Travelling Salesman Problem

Srour

Othman

Hamdan

2014

Advances in Computer Engineering

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The water flow-like algorithm (WFA) is a relatively new metaheuristic that performs well on the object grouping problem encountered in combinatorial optimization. This paper presents a WFA for solving the travelling salesman problem (TSP) as a graph-based problem. The performance of the WFA on the TSP is evaluated using 23 TSP benchmark datasets and by comparing it with previous algorithms. The experimental results show that the proposed WFA found better solutions in terms of the average solution and the percentage deviation of the average solution from the best-known solution.

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“…The two important parts of this specific implementation of the GA trained ANN for XOR problem are the implementations of the crossover and mutation operations [1]. First, the structure of the chromosome is shown in Figure 7.…”

Section: Genetic Algorithm Training For Xor Problemmentioning

confidence: 99%

Comparative Analysis of ANN trained with Back Propagation and Genetic Algorithm for Implementation of Exclusive-OR Problem

Kaur

Holland

2009

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

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In this paper, an intelligent system using an artificial neural network uses a genetic algorithm as a training method. This hybrid of an artificial neural network (ANN) and a genetic algorithm (GA) results in better solutions being derived in less time than would be derived from training an ANN using the backpropagation algorithm. The new algorithm outperforms the traditional method by a significant amount. The hybrid genetic algorithm is designed and experimented against the back propagation and the number of operations to reach convergence is reduced from 4,000 epochs to less than 50 generations, resulting in a 51.2% decrease in training time. These results indicate that the GA approach is a useful tool for training an ANN.

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Performance enhancement in solving Traveling Salesman Problem using hybrid genetic algorithm

Cited by 28 publications

References 2 publications

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

A Water Flow-Like Algorithm for the Travelling Salesman Problem

Comparative Analysis of ANN trained with Back Propagation and Genetic Algorithm for Implementation of Exclusive-OR Problem

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