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针对标准粒子群算法只能搜索到目标函数一个最小值的缺点,提出多极小值粒子群算法.该算法通过在每一代粒子群中搜索极小值粒子,使得该算法中的粒子不仅具有目标函数的最小值点信息,而且还具有目标函数的极小值点信息,从而达到搜索目标函数最小值和多个极小值的目的.该算法消除了标准粒子群算法在搜索多极小值函数时全局最优粒子在不同极小值位置附近振荡的缺点,明显的提高了收敛的速率和搜索的精度.通过对典型的一维、二维和多维目标函数进行测试,证明了多极小值粒子群算法能够寻找到目标函数的全部极小值和其所在位置,且具有很强的全局收敛能力,验证了多极小值粒子群算法的有效性.
In order to solve the shortcomings of standard particle swarm optimization (PSO), which can only find a minimum value of the objective function, a multi-minima particle swarm optimization algorithm is proposed. The algorithm searches for the minimum particle in each generation of particle swarm optimization so that the particle in the algorithm has not only the goal Function minimum point information, but also has the minimum point information of the objective function, so as to achieve the purpose of searching the minimum and multiple minimum of the objective function.The algorithm eliminates the standard particle swarm optimization algorithm in the search for multi-minimum value function The global optimal particle oscillates around different minimum positions, which obviously improves the rate of convergence and the accuracy of the search.A typical one-dimensional, two-dimensional and multi-dimensional objective function is tested to prove that multi-minimum Particle swarm optimization algorithm can find all the minima and its location of the objective function, and has strong global convergence ability, which verifies the validity of the multi-minima particle swarm optimization algorithm.