Enhancing interaction in the fireworks algorithm by dynamic resource allocation and fitness-based crowdedness-avoiding strategy

The fireworks algorithm (FWA) is a newly proposed nature-inspired swarm intelligence algorithm. In this paper, two novel mechanisms are proposed to enhance the exploration capability by means of interaction among fireworks in the FWA. The dynamic resource allocation allows the algorithm to allocate the resource (the number of sparks) adaptively according to the search results and fitness ranking of the fireworks. The fitness-based crowdedness-avoiding strategy is proposed to improve the diversity of the fireworks population by sharing the fitness information among the fireworks. Experimental results on a large variety of test functions indicate that the proposed measures significantly improve the performance of the FWA, especially on complex objective functions.