An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. (February 2017)

• Record Type: Journal Article
• Authors: Zhong, Fuli; Li, Hui; Zhong, Shouming
• Is Part Of: Engineering applications of artificial intelligence. Volume 58(2016:Oct.)
• Page Start: 134
• Page End: 156
• Publication Date: 2017-02
• Subjects: Biological-inspired optimization algorithm -- Swarm intelligence -- Artificial bee colony -- Optimization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.11.005
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Artificial bee colony (ABC) is a novel swarm intelligence optimization algorithm that has been shown to be effective in solving high dimensional global optimization problem with good performance for its excellent exploration capability. It has received a great deal of attentions of researchers since it was proposed, and was employed to many application fields for its advantages of excellent global optimization ability and easy to implement. However, the basic ABC has some drawbacks like poor exploitation and slow convergence. In this paper, an improved artificial bee colony algorithm based on modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization called MNIIABC algorithm is proposed. In the proposed algorithm, a modified-neighborhood-based update operator, which contains a global-best term and a subset-best guided term, is applied in the employed bee stage to balance the exploration and exploitation. Aiming to improve the solution diversity, a subset partition method for producing perturbation term is considered. In order to enhance the exploitation of the algorithm, an independent-inheriting-search strategy is used in the onlooker stage. Experiment results tested on multiple benchmark functions show that the proposed method is effective, and has good performance. The comparison experimental results illustrate that the proposed algorithm has good solution quality and convergence characteristics. Abstract : Highlights: A modified ABC algorithm is proposed for global optimization problems. The global and subset-best guided terms are applied in the modified-neighborhood-based update operator in employed bee stage. The exploration and exploitation are balanced and adjusted with a variable factor. A proposed independent-inheriting-search strategy is used in the onlooker stage to improve the convergence speed. Multiple numerical experiments are conducted to verify the proposed algorithm.