Adaptive differential evolution with difference mean based perturbation for dynamic economic dispatch problem

Dynamic Economic Dispatch(DED) is a very well known non-linear constrained problem with non convex characteristics due to valve-point effects. Several classical approaches have been employed to find the optimal scheduling of generation units of which Differential Evolution(DE), Particle Swarm Optimization( PSO) and their variants are mostly successful, even with large number of generation units. Differential Evolution is arguably one of the most significant evolutionary techniques of global optimization known for its simplicity, fast convergence and its multifarious applications in various field of optimization including scientific and engineering fields. Recently self adaptation of DE parameters (F=step size and CR=cross-over probability) has transformed the DE algorithm into a parameter free optimizer. A new self adaptive DE, jDE proposed by J.Brest, is a robust improvement of DE, where the self adaptive parameters undergo similar operations of genetic operators. This paper aims at introducing a unique mutation strategy by modifying the existing “DE/rand/1/bin” strategy of jDE with Difference Mean Based Perturbation(DMP) technique. The algorithm addressed as ADE-DMP is basically a variant of jDE, but the modified mutation scheme ensues within the algorithm effective search of area near the current best. In this study ADE-DMP is employed to solve the DED problem considering the valve point effects and ramp-rate limits. The efficiency of the proposed method has been validated on two popular test systems of DED problem - 10 Unit and 30 Unit DED. The comparison results affirmed the superiority of ADE-DMP over other published work in this area.