An improved Particle Swarm Optimization algorithm applied to Benchmark Functions
Alfonso Uriarte, Patricia Melín, Fevrier Valdez · 2016
According to the literature of Particle Swarm Optimization (PSO), there are problems of getting stuck at local minima and premature convergence with this algorithm. A new algorithm is presented in this paper called the Improved Particle Swarm Optimization using the gradient descent method as an operator incorporated into the Algorithm, as a function to achieve the improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The Improved PSO Algorithm IPSO is applied to Benchmark Functions. The results show that there is an improvement with respect to using the conventional PSO Algorithm.