An Improved Genetic Algorithm for the Traveling Salesman Problem with Multi-Relations

Patvichaichod, Supat

doi:10.3844/jcssp.2011.70.74

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Tournament selection is the mechanism by which the tournament is conducted for the total number of individuals in each cluster and the parent is chosen for further genetic operators. The initially 100 chromosomes are chosen with an elitism of 10% (Zitzler et al, 2000;Kannaiah et al, 2011;Maulik and Bandyopadhyay, 2000;Patvichaichod, 2011). The experiment is done many times with varying number of objects and weights.…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value

Raja¹,

Bhaskaran²

2012

American Journal of Applied Sciences

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Problem statement: Encoding is one of the major factors in genetic algorithms, for a complex problem the optimality for the problem is determined. The encoding mechanism is the initial step which forms the chromosome to form the best fit individuals in the entire population. The proposed method tries to encode the initial population by the binary encoding and by gray encoding. Approach: The problem we had identified for the experiment was a Knapsack p. The chromosomes were generated for the knapsack problem with random and the individuals were clustered into in clusters. In each cluster the parent was selected and the selected chromosome was gray coded for further genetic operators. Results: By implementing gray encoding mechanism, the experiment result shows the improvement in profit when large population was used. The results were analyzed for the algorithm by implementing it by changes in population size, changes in group size and change in mutation rate. Conclusion:The Proposed genetic algorithm reduces the execution time of the algorithm by reducing one step in the genetic operators to reach the optimal solution. The best fit individual is produced in a simple process by applying a mutation reproduction operator to the gray value.

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Section: Resultsmentioning

confidence: 99%

Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value

Raja¹,

Bhaskaran²

2012

American Journal of Applied Sciences

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“…Patvichaichod and his colleagues in [10] have developed the new procedures of GAs, which is called Hybrid Encoding GAs with multi-relations, HEGAs. The HEGAs is developed by using a new encoding, which mixes the binary encoding with the integer encoding using GAs for investigate TSP problem which each of the vertices has many edges and different cost.…”

Section: Related Workmentioning

confidence: 99%

Impact of Crossover Probability on Symmetric Travel Salesman Problem Efficiency

Alsharafat

Abu-owida²

2015

Int. J. Interact. Mob. Technol.

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Abstract-Genetic algorithm (GA) is a powerful evolutionary searching technique that is used successfully to solve and optimize problems in different research areas. Genetic Algorithm (GA) considered as one of optimization methods used to solve Travel salesman Problem (TSP). The feasibility of GA in finding TSP solution is dependent on GA operators; encoding method, population size, number of generations in general. In specific, crossover and its probability play a significant role in finding possible solution for Symmetric TSP (STSP). In addition, crossover should be determined and enhanced in term reaching optimal or at least near optimal. In This paper, we spot the light on using modified crossover method called Modified sequential constructive crossover and its impact on reaching optimal solution. To justify the relevance of parameters value in solving TSP, a set comparative analysis conducted on different crossover methods values.

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“…Genetic algorithms are usually seen as function optimizers although the range of problems and areas to which genetic algorithms have been applied is very broad. They are widely used in solving many complex Non-Deterministic Polynomial (NP) problems in different domains whose time efficiency cannot be specified in polynomial time (Patvichaichod, 2011;Kannaiah et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

An Improved Clustering Based Genetic Algorithm for Solving Complex NP Problems

Sivaraj¹,

Ravichandran²

2011

Journal of Computer Science

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Problem statement:The selection process is a major factor in genetic algorithm which determines the optimality of solution for a complex problem. The selection pressure is the critical step which finds out the best individuals in the entire population for further genetic operators. The proposed algorithm tries to find out the best individuals with reduced selection pressure than standard genetic algorithm which is commonly used. Approach: The selection process is refined in the proposed algorithm by using the concept of clustering rather than traditional selection mechanisms like Roulette wheel selection, Rank selection, Tournament selection. Results: As the selection process is improved in our approach, the convergence velocity of the genetic algorithm is improved by reaching the optimal solution quickly and the optimality of the solution is also fine-tuned. Conclusion: A new variant of the standard genetic algorithm is proposed which reduces the execution time of the algorithm by gearing up the selection process to reach the most efficient solution. The fit individuals are selected for crossover and mutation in all generations thereby reaching the solution without much complex process.

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An Improved Genetic Algorithm for the Traveling Salesman Problem with Multi-Relations

Cited by 6 publications

References 9 publications

Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value

Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value

Impact of Crossover Probability on Symmetric Travel Salesman Problem Efficiency

An Improved Clustering Based Genetic Algorithm for Solving Complex NP Problems

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