Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

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.

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Modified Grey Wolf Optimization to Solve Traveling Salesman Problem

2018 International Conference on Innovation in Engineering and Technology (ICIET) ◽

10.1109/ciet.2018.8660872 ◽

2018 ◽

Author(s):

Dibbendu Singha Sopto ◽

Safial Islam Ayon ◽

M. A. H. Akhand ◽

N. Siddique

Keyword(s):

Traveling Salesman Problem ◽

Traveling Salesman ◽

Grey Wolf ◽

Grey Wolf Optimization

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Low-Carbon Job Shop Scheduling Problem with Discrete Genetic-Grey Wolf Optimization Algorithm

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686720500018 ◽

2020 ◽

Vol 19 (01) ◽

pp. 1-14

Author(s):

Jiuchun Gu ◽

Tianhua Jiang ◽

Huiqi Zhu ◽

Chao Zhang

Keyword(s):

Optimization Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Population Diversity ◽

Neighborhood Search ◽

Scheduling Problem ◽

Low Carbon ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Local Optima

The workshop scheduling has historically emphasized the production metrics without involving any environmental considerations. Low-carbon scheduling has attracted the attention of many researchers after the promotion of green manufacturing. In this paper, we investigate the low-carbon scheduling problem in a job shop environment. A mathematical model is first established with the objective to minimize the sum of energy-consumption cost and completion-time cost. A discrete genetic-grey wolf optimization algorithm (DGGWO) is developed to solve the problem in this study. According to the characteristics of the problem, a job-based encoding method is first employed. Then a heuristic approach and the random generation rule are combined to fulfill the population initialization. Based on the original GWO, a discrete individual updating method the crossover operation of the genetic algorithm is adopted to make the algorithm directly work in a discrete domain. Meanwhile, a mutation operator is adopted to enhance the population diversity and avoid the algorithm from getting trapped into the local optima. In addition, a variable neighborhood search is embedded to further improve the search ability. Finally, extensive simulations are conducted based on 43 benchmark instances. The experimental data demonstrate that the proposed algorithm can yield better results than the other published algorithms.

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