Dynamic search fireworks algorithm with covariance mutation for solving the CEC 2015 learning based competition problems

Abstract

As a revolutionary swarm intelligence algorithm, fireworks algorithm (FWA) is designed to solve optimization problems. In this paper, the dynamic fireworks algorithm with covariance mutation (dynFWACM) is proposed. After applying the explosion operator, the mutation operator is introduced, which calculates the mean value and covariance matrix of the better sparks and produces sparks according with Gaussian distribution. DynFWACM is compared with the most advanced fireworks algorithms to proof its effectiveness. In addition, 15 functions of CEC 2015 competition on learning based real-parameter single objective optimization are used to test the performance of our new proposed algorithm. The experimental results show that dynFWACM outperforms both AFWA and dynFWA, as well as the experimental results of the 15 functions given.