A Cooperative Framework for Fireworks Algorithm

TL;DR

This paper introduces CoFFWA, a cooperative framework for the fireworks algorithm that improves exploration and exploitation capabilities, outperforming several existing optimization algorithms on benchmark functions.

Contribution

The paper proposes CoFFWA, a novel cooperative framework that enhances fireworks algorithm performance by addressing selection bias and mutation inefficiencies.

Findings

CoFFWA outperforms state-of-the-art FWA variants on benchmark functions.

It surpasses artificial bee colony, differential evolution, and particle swarm optimization in convergence performance.

Experimental results demonstrate improved optimization efficiency.

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.