Research Article
A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems
@INPROCEEDINGS{10.4108/eai.27-2-2017.152257, author={Waheed Ali H. M. Ghanem and Aman Jantan}, title={A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems}, proceedings={First EAI International Conference on Computer Science and Engineering}, publisher={EAI}, proceedings_a={COMPSE}, year={2017}, month={2}, keywords={Artificial bee colony algorithm; Monarch butterfly optimization algorithm; Global Optimization problem; Computation Intelligence}, doi={10.4108/eai.27-2-2017.152257} }- Waheed Ali H. M. Ghanem
Aman Jantan
Year: 2017
A Novel Hybrid Artificial Bee Colony with Monarch Butterfly Optimization for Global Optimization Problems
COMPSE
EAI
DOI: 10.4108/eai.27-2-2017.152257
Abstract
This article introduces a novel hybrid approach between two of the meta-heuristic algorithms to solve global optimization problems. The proposed hybrid algorithm uses the butterfly adjusting operator in Monarch Butterfly Optimization (MBO) algorithm as a mutation operator to replace the employee phase of the Artificial Bee Colony (ABC) algorithm. The novel Hybrid ABC/MBO (HAM) algorithm addresses the issues of trapping in local optimal solutions, slow convergence, and low precision by improving the balance between the characteristics of exploration and exploitation. The proposed HAM algorithm is validated on eight benchmark functions, and is compared with ABC and MBO algorithms. The experimental results show that the HAM algorithm is clearly superior to both the standard ABC and MBO algorithms.
