An Opposition-based hybrid Artificial Bee Colony with Differential Evolution

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Publication Details

Author list: Worasucheep C.

Publisher: Hindawi

Publication year: 2015

Start page: 2611

End page: 2618

Number of pages: 8

ISBN: 9781479974924

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963606388&doi=10.1109%2fCEC.2015.7257210&partnerID=40&md5=cf2a1f0aab87b9caa178f6f4ddb6f3f8

Languages: English-Great Britain (EN-GB)

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Abstract

This paper presents an opposition-based hybrid Artificial Bee Colony (ABC) with Differential Evolution (DE) algorithm for solving continuous problems. The proposed algorithm, called OABCDE, employs an efficient mutation operation of DE and a crossover-like mechanism to enhance the convergence of ABC without adding parameters. The opposition-based learning routine is periodically executed to prevent being trapped in local optima. The numerical experimentation uses 16 widely-accepted nonlinear benchmark functions of different characteristics and tests at 30, 60 and 100 dimensions. The results demonstrate that OABCDE achieves a superior performance compared to the advance qABC [9] (a recent hybrid ABC and DE) and Opposition-based DE [15]. ฉ 2015 IEEE.

Keywords

Hybridization, Opposition-based