Logistic型混合自适应分数阶达尔文粒子群优化算法

Logistic-model Hybrid Adaptive Fractional Order Darwinian Partical Swarm Optimization Algorithm

摘要:

针对传统的粒子群优化算法中存在的问题及分数阶达尔文微粒群优化(FDPSO)算法收敛速度慢,收敛精度不高的问题,改进其算法中分数阶速度更新策略,同时引入Logistic型混合分数阶自适应动态调整策略,得到一种改进的自适应分数阶达尔文粒子群优化(LFDPSO)算法,并通过相应理论分析,证明了该算法在给定条件下的收敛性,并由6个经典函数的数值测验表明,Logistic型混合自适应分数阶达尔文粒子群(LFDPSO)算法在收敛精度和收敛速度上得到了有效改善与提高,粒子在局部最优时的逃逸能力、全局寻优及智能搜索能力显著增强。

Abstract:

Aiming at the problems existing in the traditional particle swarm optimization algorithm and the problems existing in convergence speed and precision of fractional order Darwin particle swarm optimization (FDPSO) algorithm, improved the fractional order velocity update strategy of the algorithm, at the same time introduce dynamic logistic model hybrid adaptive strategy of the fractional order to form LFDPSO algorithm, through theoretical analysis and prove the convergence of the iterative algorithm under given conditions, and the experiments by six classical test functions show that the LFDPSO algorithm on the convergence accuracy and convergence speed has been further improved and enhanced, the escape ability of particles in local optimum, global optimization and intelligent search ability have achieved effective improvement.