Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 218, Issue 22, Pages 10943-10973Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2012.04.057
Keywords
Swarm Intelligence; Evolutionary algorithms; Constraint-handling; Global optimization
Categories
Funding
- Consejo Nacional de Ciencia y Tecnologia (CONACyT) [79809]
- CONACyT
Ask authors/readers for more resources
A modified Artificial Bee Colony algorithm to solve constrained numerical optimization problems is presented in this paper. Four modifications related with the selection mechanism, the scout bee operator, and the equality and boundary constraints are made to the algorithm with the aim to modify its behavior in a constrained search space. Six performance measures found in the specialized literature are employed to analyze different capabilities in the proposed algorithm such as the ability and cost to generate feasible solutions, the capacity and cost to locate the feasible global optimum solution and the competency to improve feasible solutions. Three experiments, including a comparison with state-of-the-art algorithms, are considered in the test design where twenty four well-known benchmark problems with different features are utilized. The overall results show that the proposed algorithm differs in its behavior with respect to the original Artificial Bee Colony algorithm but its performance is improved, mostly in problems with small feasible regions due to the presence of equality constraints. (C) 2012 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available