Gradient Enhanced Particle Swarm Optimization for Unconstrained Problems

This paper proposes an enhanced particle swarm optimization with gradient information (GPSO) for unconstrained optimization. Newton´s method and mutation operation are embedded in the velocity update equation to improve the effect of cognition influence and social influence, respectively. Based on numerous test function taken from the literature, computational results via a variety of experimental study showed that the GPSO approach outperformed the other techniques in terms of solution quality and convergence rate. The new algorithm proves to be extremely effective and efficient at locating best practice optimal solutions.