A modified antipredatory particle swarm optimization (MAPSO) algorithm with evasive adjustment behavior is proposed to solve the dynamic economic dispatch problem of wind power. The algorithm adds the social avoidance inertia weight to the conventional antipredatory particle swarm optimization (APSO) speed update formula. The size of inertia weight is determined by the distance between the global worst particle and other particles. After normalizing the distance, the inertia weight is controlled within the ideal range by using the characteristics of sigmoid function and linear decreasing method, which improves the ability of particles to avoid the worst solution. Then, according to the characteristics of the acceleration coefficient which can adjust the local and global searching ability of particles, acceleration coefficients of nonlinear change strategy is proposed to improve the searching ability of the algorithm. Finally, the proposed algorithm is applied to several benchmark functions and power grid system models, and the results are compared with those reported using other algorithms, which prove the effectiveness and superiority of the proposed algorithm.

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benchmark functions dynamic economic dispatch problem of wind sigmoid function linear decreasing method evasive adjustment behavior antipredatory particle swarm optimization apso speed update solution local and global searching ability particles acceleration coefficients social avoidance inertia weight nonlinear change strategy power grid system

Kai Chen,Li Han,Shuhuan Wang,Junjie Lu,Liping Shi,.Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power. 2019 (),.

Kai Chen,Li Han,Shuhuan Wang,Junjie Lu,Liping Shi,"Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power" 2019

Kai Chen,Li Han,Shuhuan Wang,Junjie Lu,Liping Shi,"Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power"