Adaptive Fireworks Algorithm Based on Two-Master Sub-population and New Selection Strategy

Abstract

Adaptive Fireworks Algorithm (AFWA) is an effective algorithm for solving optimization problems. However, AFWA is easy to fall into local optimal solutions prematurely and it also provides a slow convergence rate. In order to improve these problems, the purpose of this paper is to apply two-master sub-population (TMS) and new selection strategy to AFWA with the goal of further boosting performance and achieving global optimization. Our simulation compares the proposed algorithm (TMSFWA) with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.