Particle swarm collaborative optimization algorithm based on velocity angle
基于速度夹角的粒子群协同优化算法

Particle swarm optimization (PSO) algorithm is a stochastic global optimization technique, and it has been successfully applied in many areas. Concerning the disadvantage of the original PSO that is easily trapped in the local optimization and the convergence speed is slow in the evolution later, a particle swarm collaborative optimization algorithm based on velocity angle (V-PSCO) was proposed. The strategy of inertia weight adjustment was adopted based on cumulative distribution function of Gaussian distribution. V-PSCO was used to resolve several widely used test function optimization problems. Results show that V-PSCO has better ability of global search and can effectively improve the performance.