Artificial Bee Colony Algorithm Combined with Uniform Design
- 147 Downloads
As artificial bee colony algorithm is sensitive to the initial solutions, and is easy to fall into local optimum and premature convergence, this study presents a novel artificial bee colony algorithm based on uniform design to acquire the better initial solutions. It introduces an initialization method with uniform design to replace random initialization, and selects the better ones of those initial bees generated by the initialization method as the initial bee colony. This study also introduces a crossover operator based on uniform design, which can search evenly the solutions in the small vector space formed by two parents. This can increase searching efficiency and accuracy. The best two of the offsprings generated by the crossover operator based on uniform design are taken as new offsprings, and they are compared with their parents to determine whether to update their patents or not. The crossover operator can ensure that the proposed algorithm searches uniformly the solution space. Experimental results performed on several frequently used test functions demonstrate that the proposed algorithm has more outstanding performance and better global searching ability than standard artificial bee colony algorithm.
KeywordsBee colony Artificial bee colony Uniform design Uniform crossover
This research was supported by National Natural Science Foundation of China (No. 61841603), Guangxi Natural Science Foundation (No. 2018JJA170050), Improvement Project of Basic Ability for Young and Middle-aged Teachers in Guangxi Colleges and Universities (No. 2017KY0541), and Open Foundation for Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data Processing (No. 2017CSOBDP0301).
- 1.Cao, Y., et al.: An improved global best guided artificial bee colony algorithm for continuous optimization problems. Clust. Comput. 2018(2018), 1–9 (2018)Google Scholar
- 5.Liu, X., Wang, Y., Liu, H.: A hybrid genetic algorithm based on variable grouping and uniform design for global optimization. J. Comput. 28(3), 93–107 (2017)Google Scholar
- 7.Zhang, J., Wang, Y., Feng, J.: Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm. Sci. World J. 2013(2013), 1–16 (2013)Google Scholar
- 9.Zhu, X., Zhang, J., Feng, J.: Multi-objective particle swarm optimization based on PAM and uniform design. Math. Probl. Eng. 2015(2), 1–17 (2015)Google Scholar
- 10.Jia, L., Wang, Y., Fan, L.: An improved uniform design-based genetic algorithm for multi-objective bilevel convex programming. Int. J. Comput. Sci. Eng. 12(1), 38–46 (2016)Google Scholar
- 11.Dai, C., Wang, Y.: A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization. Knowl. Based Syst. 85(1), 131–142 (2015)Google Scholar