A Cooperative Framework for Fireworks Algorithm
May 01, 2015 ยท Declared Dead ยท ๐ 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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