Improved quantum-inspired cuckoo search algorithm based on self-adapting adjusting of search range

Abstract

Quantum-inspired cuckoo search algorithm (QCSA) has been proved to achieve a better search capability for solving discrete optimisation problem. However, due to the fixed value of the search step size, QCSA cannot adapt to the search process of the complex nonlinear optimisation problem. In order to solve this problem, the paper proposes an improved QCSA based on self-adapting adjusting of search range called IQCSA. The evolution speed factor and aggregation degree factor are firstly introduced into the proposed algorithm. In each iteration process, IQCSA can adjust the search step size dynamically according to the current evolution speed factor and aggregation degree factor, which will provide the search process with more dynamic adaptability. In addition, the proposed algorithm employs quantum Hadamard gate to make population mutation more flexibility, which enhances the population diversity in search space. The benchmark function test experiments demonstrate that, IQCSA outperforms the basic QCSA in terms of both search capability and optimisation efficiency.