This paper proposes an improved version of
the random drift particle swarm optimization algorithm for
solving the economic dispatch problem. The improvement
is achieved through adding a crossover operation followed
by a greedy selection process while replacing the mean
best position of the particles with the personal best position
of each particle in the velocity updating equation. The
improved algorithm is also augmented with a self-adaption
mechanism that eliminates the need for tuning the algorithm
parameters based on characteristics of the considered optimization
problem. Practical features such as valve point
effects, prohibited operating zones, multiple fuel options,
and ramp rate limits are considered in the mathematical
formulation of the economic dispatch problem. In order to
demonstrate the efficacy of the proposed algorithm, five
benchmark test systems are utilized. The obtained results
showed that the improved random drift particle swarm optimization
algorithm is capable of providing superior results
compared to the original algorithm and the state of the art
techniques proposed in previous literature.