Abstract: Due to the insufficiency of the global optimization ability for the basic fireworks explosion algorithm (FEA for short),which results in the premature convergence of FEA easily.In the paper,a mechanism of opposition-based learning was introduced into the FEA to generate opposition-based population,which can expand the scope of exploration of the algorithm.In addition,an adaptive explosion radius was also assigned to the individual based on individual’s fitness value.The above two strategies are integrated into the FEA to form an adaptive fireworks explosion algorithm using opposition-based learning (AFEAOL).The AFEAOL is compared with other four swarm intelligence algorithms to validate the algorithm’s efficiency on twelve classic test instances,and the experimental results demonstrate that the AFEAOL algorithm has a significant performance advantage over other three peer algorithms.

Key words: Opposition-based learning,Adaptive explosion radius,Fireworks explosion algorithm