Memetic quantum evolution algorithm for global optimization

Quantum-inspired heuristic search algorithms have attracted considerable research interest in recent years. However, existing quantum simulation methods are still limited on the basis of particle swarm optimizer. This paper explores the principle of memetic computing to develop a novel memetic quantum evolution algorithm for solving global optimization problem. First, we design a quantum theory-based memetic framework to handle multiple evolutionary operators, in which multiple units of different kinds of algorithmic information are harmoniously combined. Second, we propose the memetic evolutionary operator and the quantum evolutionary operator to complete the balance between the global search and the local search. The memetic evolutionary operator emphasizes meme diffusion by the shuffled process to enhance the global search ability. The quantum evolutionary operator utilizes an adaptive selection mechanism for different potential wells to tackle the local search ability. Furthermore, the Newton’s gravity laws-based gravitational center and geometric center as two important components are introduced to improve the diversity of population. These units can be recombined by means of different evolutionary operators that are based on the synergistic coordination between exploitation and exploration. Through extensive experiments on various optimization problems, we demonstrate that the proposed method consistently outperforms 11 state-of-the-art algorithms.