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