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针对标准粒子群(PSO)算法在复杂问题上收敛速度慢和早熟收敛的缺点,提出一种基于速度扰动的高斯学习粒子群优化算法(PGPSO).新算法中,首先在速度更新公式中添加速度扰动项,使得每次迭代进化时粒子速度增量比标准PSO更大,一方面加快了算法的收敛速度,另一方面又减缓了粒子速度快速降低的趋势,有效地维持了种群的多样性;同时引入高斯学习的概念,当算法陷入局部最优时,对全局最优粒子在搜索空间进行高斯学习,以增强算法逃离局部最优的能力.基准测试函数的实验结果表明,相较一些国际上知名的粒子群算法,新算法不仅能提高收敛速度、增强全局搜索能力,而且能有效提高解的精度和稳定性.
In order to overcome the shortcomings of the standard particle swarm optimization (PSO), such as slow convergence and premature convergence on complex problems, a Gaussian Learning Particle Swarm Optimization (PGPSO) algorithm based on velocity perturbation is proposed. In the new algorithm, the velocity update formula The perturbation term makes the increment of particle velocity larger than the standard PSO at each iteration, which accelerates the convergence rate of the algorithm and slows the rapid decrease of particle velocity, and effectively maintains the diversity of the population. At the same time, the concept of Gaussian learning is introduced, and when the algorithm falls into the local optimum, Gaussian learning is performed on the global optimal particle in the search space to enhance the ability of the algorithm to escape from the local optimum.Experimental results of the benchmark test function show that compared with some international Well-known particle swarm algorithm, the new algorithm can not only improve the convergence rate and enhance the global search capability, but also effectively improve the accuracy and stability of the solution.