A hybrid approach to artificial bee colony algorithm. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Ma, Lianbo; Zhu, Yunlong; Zhang, Dingyi; Niu, Ben
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 387
• Page End: 409
• Publication Date: 2016-02
• Subjects: Artificial bee colony algorithm -- Varying population -- Life cycle -- Comprehensive learning; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1851-x
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell's search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.