Non-convex economic load dispatch using particle swarm optimization with elevated search and addressed operators

This paper presents a stochastic-based method to solve the non-convex economic load dispatch problem for minimizing fuel cost of thermal units. Several measures have been taken to improve the computational efficiency of the conventional Particle Swarm Optimization (PSO). The cognitive behavior of particle is split in two components to check the best and poor experience of particles. The control parameters of the PSO are tuned to their optimal values. The modulations in inertia weight are controlled in accordance to a new truncated sinusoidal function. A correction algorithm has been proposed to transform infeasible particles into feasible ones. The effectiveness of the proposed PSO is tested on different standard thermal generating systems. The application results seem to be promising when compared with other existing methods.