Enhanced Constrained Artificial Bee Colony Algorithm for Optimization Problems
Journal: The International Arab Journal of Information Technology (Vol.14, No. 2)Publication Date: 2017-03-01
Authors : Soudeh Babaeizadeh; Rohanin Ahmad;
Page : 246-253
Keywords : ABC; constrained optimization; swarm intelligence; search equation.;
Abstract
Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This drawback can be even more significant when constraints are also involved. To address this issue, an Enhanced Constrained ABC algorithm (EC-ABC) is proposed for Constrained Optimization Problems (COPs) where two new solution search equations are introduced for employed bee and onlooker bee phases respectively. In addition, both chaotic search method and opposition-based learning mechanism are employed to be used in population initialization in order to enhance the global convergence when producing initial population. This algorithm is tested on several benchmark functions where the numerical results demonstrate that the EC-ABC is competitive with state of the art constrained ABC algorithm.
Other Latest Articles
- UCOM Offline Dataset-An Urdu Handwritten Dataset Generation
- QoS Adaptation for Publish/Subscribe Middleware in Real-Time Dynamic Environments
- An Approach for Identifying Failure-Prone State of Computer System
- Global Software Development Geographical Distance Communication Challenges
- COST-BENEFIT ANALYSIS OF JOURNALS SUBSCRIPTION AT NEHRU LIBRARY, CCSHAU, HISAR, HARYANA
Last modified: 2019-05-08 16:49:10