An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow

This paper presents an Enhanced Self-adaptive Differential Evolution with Mixed Crossover (ESDE-MC) algorithm to solve the multiobjective optimal power flow problems with conflicting objectives that reflect the minimization of total production cost, emission pollution, L-index, and active power loss. In this algorithm, a combination of eigenvector and binomial crossovers has been used to move the current population towards better search positions to provide good quality solutions. Besides, an adaptive dynamic parameter adjusting strategy is adopted to obtain the appropriate parameter settings in differential evolution algorithm during the evolution process. Further, an external archive is used to preserve all the nondominated solutions evaluated in each iteration and a fuzzy decision-making technique is applied to extract the best compromise solution from all the nondominated solutions in the archive set. Finally, in order to investigate the usefulness of the proposed algorithm, IEEE 30-bus, IEEE 57-bus and Algerian 59-bus systems with different single and multiobjective OPF problems have been solved and the simulation results are evaluated and compared with the other algorithms recently reported in the literature. The results indicate that the proposed algorithm is competent, effective and quite suitable for solving single/multi objective optimal power flow problems. (C) 2017 Elsevier B.V. All rights reserved.