A Novel Discrete Grey Wolf Optimizer for Solving the Bounded Knapsack Problem
Grey Wolf Optimizer (GWO) is a recently proposed metaheuristic optimizer inspired by the leadership hierarchy and hunting mechanism of grey wolves. In order to solve the bounded knapsack problem by the GWO, a novel Discrete Grey Wolf Optimizer (DGWO) is proposed in this paper. On the basis of DGWO, the crossover strategy of the genetic algorithm is introduced to enhance its local search ability, and infeasible solutions are processed by a Repair and Optimization method based on the greedy strategy, which could not only ensure the effectiveness but also speed up the convergence. Experiment using three kinds of large-scale instances of the bounded knapsack problem is carried out to verify the validity and stability of the DGWO. By comparing and analyzing the results with other well-established algorithms, computational results show that the convergence speed of the DGWO is faster than that of other algorithms, solutions of these instances of the bounded knapsack problem are all well obtained with approximation ratio bound close to 1.
KeywordsBounded knapsack problem Grey Wolf Optimizer Genetic algorithm Repair and optimization method
This work was supported by the Scientific Research Project Program of Colleges and Universities in Hebei Province (ZD2016005), and the Natural Science Foundation of Hebei Province (F2016403055).
- 2.Masadeh, R., Yassien, E., Alzaqebah, A., et al.: Grey wolf optimization applied to the 0/1 knapsack problem. Int. J. Comput. Appl. 169(5), 11–15 (2017)Google Scholar
- 3.Sharma, S., Salgotra, R., Singh, U.: An enhanced grey wolf optimizer for numerical optimization. In: International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1–6 (2017)Google Scholar
- 5.Hatta, N.M., Zain, A.M., Sallehuddin, R., et al.: Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artif. Intell. Rev. May 2018. https://doi.org/10.1007/s10462-018-9634-2
- 15.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942–1948 (1995)Google Scholar
- 19.Moradi, M., Badri, A., Ghandehari, R.: Non-convex constrained economic dispatch with valve point loading effect using a grey wolf optimizer algorithm. In: 2016 6th Conference on Thermal Power Plants (CTPP), pp 96–104. IEEE (2016)Google Scholar
- 20.Chandra, M., Agrawal, A., Kishor, A., Niyogi, R.: Web service selection with global constraints using modified Grey Wolf optimizer. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 1989–1994. IEEE (2016)Google Scholar
- 21.He, Y.C., Wang, X.Z., Zhao, S.L., Zhang, X.L.: Design and applications of discrete evolutionary algorithm based on encoding transformation. Ruan Jian Xue Bao/J. Softw. 29(9) (2018). (in Chinese). http://www.jos.org.cn/1000-9825/5400.htm
- 26.He, Y.C., Song, J.M., Zhang, J.M., et al.: Research on genetic algorithm for solving static and dynamic knapsack problems. Appl. Res. Comput. 32(4), 1011–1015 (2015). (in Chinese)Google Scholar