JOURNAL ARTICLE

An enhanced hybrid genetic algorithm for solving traveling salesman problem

Zeravan Arif AliSubhi Ahmed RasheedNabeel No’man Ali

Year: 2020 Journal:   Indonesian Journal of Electrical Engineering and Computer Science Vol: 18 (2)Pages: 1035-1035   Publisher: Institute of Advanced Engineering and Science (IAES)

Abstract

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

Keywords:
Travelling salesman problem Genetic algorithm Local search (optimization) Algorithm Mathematical optimization 2-opt Christofides algorithm Computer science Population Population-based incremental learning Mutation Limit (mathematics) Mathematics

Metrics

10
Cited By
1.17
FWCI (Field Weighted Citation Impact)
27
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Vehicle Routing Optimization Methods
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering
Scheduling and Timetabling Solutions
Social Sciences →  Decision Sciences →  Management Science and Operations Research

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