A Cooperative Framework for Fireworks Algorithm

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 14; no. 1; pp. 27 - 41
• Main Authors: Shaoqiu Zheng, Junzhi Li, Janecek, Andreas, Ying Tan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Biomimetics - methods; Computer Simulation; Cooperative Behavior; cooperative strategy; Crowding; Exploitation; Exploration; explosion amplitude; Explosions; Fireworks; Fireworks algorithm; Fitness; Gaussian processes; Genetic algorithms; Models, Statistical; Mutation; Operators (mathematics); Particle swarm optimization; Reproductive fitness; Swarm intelligence
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2497227

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 current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other 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 significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.