Enhancement of economic dispatch problem using self adaptive real-coded genetic algorithm

Abstract EN

In this paper, a self-adaptive real-coded genetic algorithm (SARGA) is implemented to solv the economic dispatch (ED) problem with valve-point effects.

The self-adaptation is achieved by means of tournament selection along with simulated binary crossover (SBX).

The selection process has a power exploration capability by creating tournaments between two solutions the better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden.

The population diversity is introduced by making use o distribution index in SBX operator to create a better offspring.

The SARGA is applied t solve ED problem with valve-point effects which has large number of local minima.

Th numerical results demonstrate that the proposed method can find a solution towards th global optimum and compares favorably with other recent methods in terms of solution quality, handling constraints and computation time.