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Algorithms 2016, 9(3), 43;

Opposition-Based Adaptive Fireworks Algorithm

Department of Computer Information Engineering, Guangdong Technical College of Water Resource and Electrical Engineering, Guangzhou 510635, China
Academic Editor: Yun-Chia Liang
Received: 2 April 2016 / Revised: 13 June 2016 / Accepted: 4 July 2016 / Published: 8 July 2016
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost. View Full-Text
Keywords: opposition-based learning; fireworks algorithm; swarm intelligence; global optimization opposition-based learning; fireworks algorithm; swarm intelligence; global optimization

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gong, C. Opposition-Based Adaptive Fireworks Algorithm. Algorithms 2016, 9, 43.

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