关键词: 引力搜索算法（GSA）, Lé, vy Flight, 惯性权重

Abstract: Gravitational search algorithm (GSA) is a novel optimization algorithm based on the law of gravity and mass interactions. Some studies show that GSA can obtain more superior results than classical optimization algorithms in most cases. But the standard GSA is easy to be trapped into local optimum and premature convergence. This paper proposes an improved weighted algorithm based on Lévy Flight and inertia weight, named QmuGSA, to strength global search capacity of GSA. The new algorithm can expand agents’ search space and increase the diversity of population by using the characteristics of random walk of Lévy Flight, it is easier to jump out of local optimal point. The numerical results in four benchmark functions demonstrate that the new algorithm can effectively overcome the defects of premature and low convergence precision of the standard GSA, has better optimization precision and global convergence performance, and can solve some complex function optimization problems.

Key words: gravitational search algorithm (GSA), Lévy Flight, inertia weight