Fireworks Algorithm Based on Opposition-Based Learning and Quantum Optimization Strategy

Abstract

Aiming at the bottleneck of optimization performance and slow convergence speed of fireworks algorithm, a new fireworks algorithm (FWA) is proposed by integrating Opposition-Based Learning and Quantum Optimization strategy (OQFWA). The new algorithm optimizes the original fireworks algorithm in the aspects of the selection of sparks and the local improvement of the optimal individuals, which effectively improves the convergence accuracy and speed of the algorithm. The simulation results of extremum optimization of typical test functions with different characteristics show that the fireworks algorithm, which integrates the Opposition-Based Learning and Quantum Optimization strategy, and has good optimization performance.