A Novel Memetic Algorithm Based on Real-Observation Quantum-Inspired Evolutionary Algorithms

To enhance the local search capability of quantum-inspired evolutionary algorithm, a novel Memetic Algorithm based on real-observation Quantum-inspired evolutionary algorithms (MArQ) was proposed. MArQ is a hybrid algorithm combining QIEA with local search techniques. In MArQ, QIEA was used to explore the whole solution space and tabu search was employed to exploit the neighboring domains of the searched best solutions. Several bench complex functions and an application example of reactive power optimization in power systems were applied to test the MArQ performances. Experimental results show that MArQ is superior to the real-observation quantum-inspired evolutionary algorithm and several optimization algorithms reported, in terms of search capability and stability.