Enhanced Fireworks Algorithm

In this paper, we present an improved version of the recently developed Fireworks Algorithm (FWA) based on several modifications. A comprehensive study on the operators of conventional FWA revealed that the algorithm works surprisingly well on benchmark functions which have their optimum at the origin of the search space. However, when being applied on shifted functions, the quality of the results of conventional FWA deteriorates severely and worsens with increasing shift values, i.e., with increasing distance between function optimum and origin of the search space. Moreover, compared to other metaheuristic optimization algorithms, FWA has high computational cost per iteration. In order to tackle these limitations, we present five major improvements of FWA: (i) a new minimal explosion amplitude check, (ii) a new operator for generating explosion sparks, (iii) a new mapping strategy for sparks which are out of the search space, (iv) a new operator for generating Gaussian sparks, and (v) a new operator for selecting the population for the next iteration. The resulting algorithm is called Enhanced Fireworks Algorithm (EFWA). Experimental evaluation on twelve benchmark functions with different shift values shows that EFWA outperforms conventional FWA in terms of convergence capabilities, while reducing the runtime significantly.