Paper Info
Title
Best neighbor-guided artificial bee colony algorithm for continuous optimization problems
Abstract
As a relatively recent invented swarm intelligence algorithm, artificial bee colony (ABC) becomes popular and is powerful for solving the tough continuous optimization problems. However, the weak exploitation has greatly affected the performance of basic ABC algorithm. Meanwhile, keeping a proper balance between the exploration and exploitation is critical work. To tackle these problems, this paper introduces a best neighbor-guided ABC algorithm, named NABC. In NABC, the best neighbor-guided solution search strategy is proposed to equilibrate the exploration and exploitation of new algorithm during the search process. Moreover, the global neighbor search operator has displaced the original random method in the scout bee phase aiming to preserve the search experiences. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2013 shifted and rotated problems) and one real-world application problem (the software defect prediction). Experimental results and comparison with the state-of-the-art ABC variants indicate that NABC is very competitive and outperforms the other algorithms.
Year
DOI
Venue
2019
10.1007/s00500-018-3473-6
soft computing
Keywords
Field
DocType
Artificial bee colony (ABC), Continuous optimization problems, Best neighbor-guided search, Global neighbor search, Software defect prediction
Continuous optimization,Artificial bee colony algorithm,Mathematical optimization,Computer science,Swarm intelligence,Software bug,Operator (computer programming)
Journal
Volume
Issue
ISSN
23.0
SP18.0
1433-7479
Citations 
PageRank 
References 
2
0.36
38
Authors
3
Name
Order
Citations
PageRank
Hu Peng14613.63
Changshou Deng23910.80
Zhijian Wu324718.55