论文部分内容阅读
针对粒子群算法(particle swarm optimization,PSO)收敛速度慢和早熟收敛的问题,提出一种具有高斯扰动的最优粒子引导粒子群优化算法(OGPSO).该算法通过在粒子的速度更新公式上移除自我认知部分,增加局部最优粒子控制的高斯扰动项来实现改进PSO算法.通过移除自我认知部分,使种群中的粒子主要受当前全局最优粒子引导;通过增加高斯扰动项,又提供了一种防止粒子陷入局部最优点的机制.两种改进措施相结合,既加快了收敛速度,又避免了早熟收敛的问题.在典型测试函数集上的仿真实验结果和与其它经典及新近改进PSO算法的对比实验结果,均表明本文算法有较好的寻优性能及稳定性.
To solve the problem of slow convergence and premature convergence of particle swarm optimization (PSO), an optimal Particle Guidance Particle Swarm Optimization (OGPSO) algorithm with Gaussian perturbation is proposed. The algorithm is based on the particle velocity update formula In addition to the self-cognitive part, the Gaussian perturbation term of local optimal particle control is increased to improve the PSO algorithm. By removing the self-cognitive part, the particles in the population are mainly guided by the current global optimal particle. By adding Gaussian perturbation terms, But also provides a mechanism to prevent the particle from falling into the local optimum point.The combination of the two improvement measures not only speeds up the convergence speed but also avoids the premature convergence problem.The simulation results on the typical test function set and the comparison with other classical and The results of comparative experiments of the newly improved PSO algorithm show that the proposed algorithm has better performance and stability.