Economic load dispatch using hybrid MPBBO-SQP algorithm

Abstract

Solving economic load dispatch (ELD) problems of electric power systems means finding the optimal output of the generating units, so that the load demand can be satisfied with the lowest possible operating cost, and without violating any design constraint. Recently, many nature-inspired algorithms have been successfully used to solve these highly constrained non-linear and non-convex ELD problems without facing much efforts as regularly happens with the conventional optimization algorithms. Biogeography-based optimization (BBO) algorithm is a new populationbased evolutionary algorithm (EA). As per the conducted studies in the literature, BBO has good exploitation, but it lacks exploration. In this study, the poor exploration level of BBO is enhanced by hybridizing it with theMetropolis criterion of the simulated annealing (SA) algorithm in order to have more control on the migrated individuals; and hence the first phase of this proposed algorithm is calledMetropolis BBO (in short MpBBO). The second hybridization phase is done by combining the strength of the Sequential Quadratic Programming (SQP) algorithm with MpBBO to have a new superior algorithm called MpBBO-SQP, where the best solutions per each generation of MpBBO phase is fine-tuned by SQP phase. The performance of MpBBO-SQP is evaluated using three test cases with five different cooling strategies of SA. The results obtained show that MpBBO-SQP outperforms different BBO models as well as many other competitive algorithms presented in the literature.