Hybrid Artificial Bee Colony algorithm with Differential Evolution

• Published in: Applied soft computing Vol. 58; pp. 11 - 24
• Main Authors: Jadon, Shimpi Singh, Tiwari, Ritu, Sharma, Harish, Bansal, Jagdish Chand
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2017
• Subjects: Artificial Bee Colony; Differential Evolution; Hybridization; Optimization; Swarm intelligence; Artificial Bee Colony; Swarm intelligence; Hybridization; Differential Evolution; Optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.04.018

[Display omitted] •A hybrid algorithm (HABCDE) based on ABC and DE is proposed.•The performance of the proposed method is investigated on well-known benchmark and some popular real world problems.•The obtained results are compared with four state of the art, three hybrid and four other variants of ABC algorithms.•Experiments on benchmark and real world problems show the superiority of HABCDE.•HABCDE algorithm provides faster convergence while maintaining diversity. Artificial Bee Colony (ABC) and Differential Evolution (DE) are two very popular and efficient meta-heuristic algorithms. However, both algorithms have been applied to various science and engineering optimization problems, extensively, the algorithms suffer from premature convergence, unbalanced exploration-exploitation, and sometimes slow convergence speed. Hybridization of ABC and DE may provide a platform for developing a meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation capabilities. This paper proposes a hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC and DE. In the proposed hybrid algorithm, Hybrid Artificial Bee Colony with Differential Evolution (HABCDE), the onlooker bee phase of ABC is inspired from DE. Employed bee phase is modified by employing the concept of the best individual while scout bee phase has also been modified for higher exploration. The proposed HABCDE has been tested over 20 test problems and 4 real-world optimization problems. The performance of HABCDE is compared with the basic version of ABC and DE. The results are also compared with state-of-the-art algorithms, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) and Spider Monkey Optimization (SMO) to establish the superiority of the proposed algorithm. For further validation of the proposed hybridization, the experimental results are also compared with other hybrid versions of ABC and DE, namely ABC-DE, DE-BCO and HDABCA and with modified ABC algorithms, namely Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC) and modified ABC (MABC). Results indicate that HABCDE would be a competitive algorithm in the field of meta-heuristics.