Abstract
The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.
Highlights
In order to solve optimization problems in many fields, swarm intelligence-based optimization algorithms have attracted much attention [1,2,3,4,5,6] in recent years
The whale optimization algorithm (WOA) [7] is a new type of swarm intelligence optimization algorithm proposed by Mirjalili and Lewis in 2016 which is inspired by the unique predation behavior of humpback whales
Based on the characteristics of the traveling salesman problem (TSP) [25] and the optimization mechanism of the WOA, a discrete whale optimization algorithm with variable neighborhood search (VDWOA) for solving larger-scale TSP is designed in this paper
Summary
In order to solve optimization problems in many fields, swarm intelligence-based optimization algorithms have attracted much attention [1,2,3,4,5,6] in recent years. Huang et al (2020) [14] proposed an improved WOA algorithm based on chaotic weights and elite guidance strategies. Ding et al (2019) [15] combined the WOA with adaptive weight and simulated annealing The former is used to adjust the convergence speed and later is used to improve the global optimization ability of the WOA algorithm. For the traveling salesman problem, Ahmed and Kahramanli (2018) [23] used the classic WOA and the gray wolf optimization algorithm to solve smaller-scale problems, respectively. Based on the characteristics of the traveling salesman problem (TSP) [25] and the optimization mechanism of the WOA, a discrete whale optimization algorithm with variable neighborhood search (VDWOA) for solving larger-scale TSP is designed in this paper. Experimental results are analyzed to verify the effectiveness of the proposed VDWOA algorithm
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