Reactive power optimization based on improved social cognitive optimization algorithm

Social cognitive optimization (SCO) algorithm is presented based on human intelligence with the social cognitive theory. This paper improves the SCO algorithm with shrinking search in the Simulating Fisher fishing Optimization algorithm. Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Particle swarm optimization (PSO) algorithm has high convergence speed and is easy to implement, but it also exists precocious phenomenon. Considering minimum network loss as the objective function, make the simulation in standard IEEE-14 and IEEE-30 node system. The results show that the improved social cognitive optimization algorithm can achieve a better global optimal solution compared with PSO and SCO algorithms.