Fireworks algorithm framework for Big Data optimization

This paper presents a novel optimization framework based on the Fireworks Algorithm for Big Data Optimization problems. Indeed, the proposed framework is composed of two optimization algorithms. A single objective Fireworks Algorithm and a multi-objective Fireworks Algorithm are proposed for solving the Big Optimization of Signals problem “Big-OPT” which belongs to the Big Data Optimization problems class. The single objective Fireworks Algorithm adopts a modified search mechanism to ensure rapidity and preserve the explorative capacities of the basic Fireworks Algorithm. Afterward, the algorithm is extended to handle multi-objective optimization of Big-OPT with a supplementary special sparks phase and a novel strategy for next generation selection. To validate the performance of the framework, extensive tests on six EEG datasets are performed. The framework is also compared with several approaches from recent state of the art. The study concludes the competitive performance of the proposed framework in comparison with the other techniques reported in this paper.