Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

School of Computer & Security Science

RAS ID

10099

Comments

This article was originally published as: Zeng, F., Decraene, J., Low, M., Hingston, P. F., Wentong, C., Suiping, Z., & Chandramohan, M. (2010). Autonomous Bee Colony Optimization for Multi-objective Function. Proceedings of IEEE Congress on Evolutionary Computation. (pp. 1279-1286). . Barcelona International Convention Centre, Barcelona, Spain. IEEE. Original article available here

Abstract

An Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems is proposed. In contrast with previous Bee Colony algorithms, A-BCO utilizes a diversity-based performance metric to dynamically assess the archive set. This assessment is employed to adapt the bee colony structures and flying patterns. This self-adaptation feature is introduced to optimize the balance between exploration and exploitation during the search process. Moreover, the total number of search iterations is also determined/optimized by A-BCO, according to user pre-specified conditions, during the search process. We evaluate A-BCO upon numerical benchmark problems and the experimental results demonstrate the effectiveness and robustness of the proposed algorithm when compared with the Non-dominated Sorting Genetic Algorithm II and the latest Multi-objective Bee Colony Algorithm proposed to date.

DOI

10.1109/CEC.2010.5586057

 
COinS
 

Link to publisher version (DOI)

10.1109/CEC.2010.5586057