引力搜索算法(gravitational search algorithm,GSA)是模拟万有引力定律进行搜索的一种新颖的优化算法,已有研究表明GSA算法相比一些传统的优化算法拥有较好的收敛性能,但其缺乏有效的全局寻优机制,易于被局部极值吸引,从而陷入早熟收敛。因此提出了一种基于Lévy Flight和权值惯性递减的引力搜索算法QmuGSA,以加强算法的全局寻优能力。该算法通过Lévy Flight独特的不均匀随机游走的机制扩大粒子的搜索范围,增加种群多样性,从而更容易跳出局部最优点。通过4个标准测试函数对所提算法进行了仿真测试,结果表明所提算法能够有效克服基本引力搜索算法易早熟、收敛精度低等缺陷,具有较好的寻优精度和全局收敛性能,能够解决一些复杂函数的优化问题。 Gravitational search algorithm (GSA) is a novel optimization algorithm that simulates the law of universal gravitation. Studies have shown that GSA has better convergence performance than some traditional optimization algorithms, but it lacks effective global Search mechanism, easy to be attracted by the local extreme, which plunged into premature convergence. Therefore, a gravitation search algorithm QmuGSA based on Lévy Flight and weight inertia reduction is proposed to enhance the global optimization ability of the algorithm. The algorithm expands the search range of particles through the unique non-uniform random walk mechanism of Lévy Flight, increases the population diversity and makes it easier to jump out of the local optimal point. The proposed algorithm is tested by four standard test functions. The results show that the proposed algorithm can effectively overcome the shortcomings of the basic gravitational search algorithm, such as premature convergence, low convergence accuracy, better accuracy and global convergence performance, and can solve the problem Some complex function optimization problem.