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- Title
An Improve Grey Wolf Optimizer Algorithm for Traveling Salesman Problems.
- Authors
Zhinan Xu; Xiaoxia Zhang
- Abstract
The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey wolf optimization (GWO) algorithm has shown outstanding performance in many practical applications. However, it is inclined towards premature convergence. This paper proposes an improved GWO (I-GWO) algorithm, which hybridizes GWO with genetic algorithms (GA) for the TSP. The main feature of the I-GWO algorithm is that it can make full use of the advantages of the GWO algorithm and the GA algorithm to make up for their respective shortcomings. Moreover, to make the GWO suitable for solving the TSP, both the 2-opt operator strategy and hamming distance h ave been designed to implement the discrete GWO directly. Additionally, to increase the diversity of solutions by expanding the search space, we present a new population update strategy with crossover and mutation operations in the next iteration. Meanwhile, the integration of the Simulated Annealing (SA) algorithm into the Improved Grey Wolf Optimizer (I-GWO) enhances its local search capabilities. Experimental results show that the I-GWO algorithm competes with established optimal methods for solving the TSP, suggesting its potential for different TSP variants and logistic transport domains.
- Subjects
GREY Wolf Optimizer algorithm; TRAVELING salesman problem; HAMMING distance; SIMULATED annealing; NP-hard problems
- Publication
IAENG International Journal of Computer Science, 2024, Vol 51, Issue 6, p602
- ISSN
1819-656X
- Publication type
Article