A Cooperative Framework for Fireworks Algorithm

May 01, 2015 ยท Declared Dead ยท ๐Ÿ› IEEE/ACM Transactions on Computational Biology & Bioinformatics

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Authors Shaoqiu Zheng, Junzhi Li, Andreas Janecek, Ying Tan arXiv ID 1505.00075 Category cs.NE: Neural & Evolutionary Citations 92 Venue IEEE/ACM Transactions on Computational Biology & Bioinformatics Last Checked 4 months ago
Abstract
This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the selection strategy lead to the contribution of the firework with the best fitness (core firework) for the optimization overwhelms the contributions of the rest of fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which can greatly enhance the exploitation ability of non-core fireworks by using independent selection operator and increase the exploration capacity by crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions suggest that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, the standard particle swarm optimization (SPSO) in 2007 and the most recent SPSO in 2011 in term of convergence performance.
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