A Novel Evolutionary Optimization Technique for Solving Optimal Reactive Power Dispatch Problems

Biogeography based optimization (BBO) is an efficient and powerful stochastic search technique for solving optimization problems over continuous space. Due to excellent exploration and exploitation property, BBO has become a popular optimization technique to solve the complex multi-modal optimization problem. However, in some cases, the basic BBO algorithm shows slow convergence rate and may stick to local optimal solution. To overcome this, quasi-oppositional biogeography based-optimization (QOBBO) for optimal reactive power dispatch (ORPD) is presented in this study. In the proposed QOBBO algorithm, oppositional based learning (OBL) concept is integrated with BBO algorithm to improve the search space of the algorithm. For validation purpose, the results obtained by the proposed QOBBO approach are compared with those obtained by BBO and other algorithms available in the literature. The simulation results show that the proposed QOBBO approach outperforms the other listed algorithms.

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