Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables

This paper presents a Novel version of real-coded quantum evolutionary algorithm (NRQEA) to solve global numerical optimization with continuous variables. Complementary mutation operator, which is designed based on the specific configuration of real-coded chromosome and the gradient informance of objective function, is used to update chromosomes and realize a balance between exploration and exploitation. Technique of reducing the search space, which is implemented based on the evolutionary process of algorithm, is adopted to improve the convergence rate. Simulation results on benchmark functions show that the algorithm proposed is more suitable for global numerical optimization with continuous variables than the compared algorithms, and has the characteristics of rapid convergence, good global search ability and stability.