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Application of Global Best Imperialist Competition Algorithm for Multi-objective Reactive Power Optimization

This study proposes a multi-objective global best imperialist competition algorithm (MOGBICA) for solving two dual-objective reactive power optimization problems. Previous research has shown that imperialist competition algorithm (ICA) has certain advantages in solving optimization problems. However, the original ICA often converges to local optima. The global best imperialist country solution is proposed to enhance the searching capability of ICA. In addition, the nondominated sorting and crowding distance methods are employed to solve multi-objective optimization problems (MOPs). The proposed approaches is studied and evaluated on the IEEE 30 bus system. The obtained results show that the MOGBICA provides better results than MOPSO and MOICA algorithm.