Adaptive biogeography based predator–prey optimization technique for optimal power flow

• An elegant strategy is developed for optimal power flow (OPF) problem.
• The method involves biography based optimization for solving the OPF problem.
• This strategy explores the search space through an adaptive mutation scheme.
• The suggested technique uses the predator–prey model for avoiding suboptimal traps.
• The results for IEEE 30 bus test system are presented.

This article presents a new approach based on a hybrid algorithm consisting of biogeography based optimization (BBO) with an adaptive mutation scheme and the concept of predator–prey optimization technique for solving the multi-objective optimal power flow problems. The adaptive mutation scheme, based on distance-to-average point diversity measure, avoids the dominance of highly probable solutions through increasing the population diversity. The predators search around the best prey in a concentrated manner, while the preys explore the solution space so as to stay away from the predators. These mechanisms enhance the exploitation and exploration capabilities of the BBO search process, provide a mean of escaping from the suboptimal solutions and force the population to arrive at the global best solution. The proposed method is tested on IEEE 30 bus test system with different objectives that reflect fuel cost minimization, loss reduction, voltage profile improvement and voltage stability enhancement. The comparison of results with those of the existing approaches illustrates the effectiveness and robustness of the suggested method.