Hybrid Algorithm Based on Phasor Particle Swarm Optimization and Firefly Algorithm.

This paper proposes a new hybrid algorithm (HBPPSO) based on the Phasor Particle Swarm Optimization (PPSO) and the Firefly Algorithm (FA) to improve particle velocity and position updates. Initially, a trigonometric updating approach is presented for nonlinearly adjusting particle velocity. The particle position is then controlled using an exponential updating technique. Following that, six groups of experiments are performed to validate the optimization performance of the proposed HFPPSO method. The phasor particle swarm optimization algorithm and particle swarm optimization are chosen as the comparison algorithms. Finally, the experimental findings show that HBPPSO unifies the strengths of managing the learning parameters in both PPSO and FA algorithms while alleviating the weakness of premature convergence, improving searching accuracy and avoiding slipping into local optimum.