Dynamically controlled particle swarm optimization for large-scale nonconvex economic dispatch problems

This paper proposes a dynamically controlled particle swarm optimization method to solve nonconvex economic dispatch problem of large dimensions. It essentially aims to improve the performance of the conventional particle swarm optimization by suggesting improved cognitive and social components of the particle's velocity through preceding and aggregate experience of the swarm, respectively. The control parameters of the governing equation are controlled dynamically by introducing new exponential functions. The overall methodology effectively regulates the velocity of the particles during their whole course of flight and results in substantial improvement. The effectiveness of the proposed method has been investigated on 40 generators and 140 generators test generating systems by considering general operational constraints. The application results show that the proposed method is very promising to solve large-dimensional economic dispatch problems.