Multi-Scale Collaborative Fireworks Algorithm

Yifeng Li, Ying Tan

Abstract: Fireworks Algorithm (FWA) is a special swarm intelligent optimization algorithm, which controls multiple subgroups of the population to search collaboratively. Instead of assigning fireworks to different local areas, we propose the multiscale collaborative firework algorithm (MSCFWA) which helps fireworks to search at coordinated scales. Since the collaboration of search scales is accomplished by restarting or adjusting fireworks whose local search are not making meaningful progress, fireworks in MSCFWA are able to exploit different local areas independently or cooperate in the same local area with different search scales. Experimental results show that the proposed strategy stably improved the overall optimization performance of fireworks algorithm on the benchmark functions of the CEC'13 competition significantly. It also shows outstanding efficiency compared with typical swarm intelligence optimization algorithms and evolutionary algorithms.