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

© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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

Access Rights

free_to_read

 
COinS
 

Link to publisher version (DOI)

10.1109/CEC.2010.5586057