Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

Abstract

In order to overcome the low solution efficiency, insufficient diversity in the later search stage, slow convergence speed and a high search stagnation possibility of differential evolution(DE) algorithm, the quantum computing characteristics of quantum evolutionary algorithm(QEA) and the divide-and-conquer idea of cooperative coevolution evolutionary algorithm(CCEA) are combined to propose an improved differential evolution(HMCFQDE) in this paper. In the proposed HMCFQDE, a new hybrid mutation strategy based on the advantages of local neighborhood mutation and SaNSDE is designed. In the early stage of the search, the local neighborhood mutation strategy with high search efficiency is used to speed up the algorithm convergence. In the later stage of the search, the SaNSDE algorithm is used to adjust the search direction in order to avoid the search stagnation. The QEA is combined with the DE to make use of the quantum chromosome encoding to enhance the population diversity, the quantum rotation to speed up the convergence speed. The CC framework is used to divide the large-scale and high-dimensional complex optimization problem into several low-dimensional optimization sub-problems, and these sub-populations are solved by independent searching among sub-populations in order to improve the solution efficiency. By comparing with other 6 algorithms in solving 6 test functions from CEC’08 under the dimensions of 100, 500 and 1000, it is proved that the proposed HMCFQDE has higher convergence accuracy and stronger stability. In particular, it has a strong ability to optimize high-dimensional complex functions. Therefore, it provides a new method for solving large-scale optimization problem.