基于速度夹角的粒子群协同优化算法

Particle swarm collaborative optimization algorithm based on velocity angle

粒子群算法(PSO)是一种随机全局优化算法,在许多领域得到了广泛应用。针对PSO存在易陷入局部极值、进化后期收敛速度缓慢的缺点,提出一种基于速度夹角的粒子群协同优化算法(V-PSCO),并且引入了一种基于高斯分布的累积分布函数的惯性权重调整策略。将V-PSCO用于几种典型函数的优化问题,结果表明,V-PSCO具有更强的全局搜索能力,优化性能明显提高。

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.