An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator
| Autor: | Ahsan Sadee Tanim, S. M. Afif Ibne Hayat, Muhammad Nomani Kabir, Sadman Sakib Choudhury, Mohammad Mainul Islam, Md. Sabir Hossain |
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| Rok vydání: | 2019 |
| Předmět: |
Genetic Algorithm
0209 industrial biotechnology Mathematical optimization Computer science Crossover MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology Crossover Operator Travelling salesman problem Set (abstract data type) Traveling Salesman Problem 020901 industrial engineering & automation Operator (computer programming) lcsh:TA1-2040 Mutation (genetic algorithm) Genetic algorithm 0202 electrical engineering electronic engineering information engineering Combinatorial optimization 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) Minimal Weight Variable Selection (genetic algorithm) |
| Zdroj: | Emitter: International Journal of Engineering Technology, Vol 7, Iss 2 (2019) |
| ISSN: | 2443-1168 2355-391X |
| DOI: | 10.24003/emitter.v7i2.380 |
| Popis: | The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm. |
| Databáze: | OpenAIRE |
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