带有Levy Flight机制的引力搜索算法

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Levy 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Levy 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 with Levy Flight Mechanism

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 algo-rithms 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 L＆#233;vy Flight and inertia weight, named QmuGSA, to strength global search capacity of GSA. The new algorithm can expand agents’search space and increase the diver-sity of population by using the characteristics of random walk of L＆#233;vy 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 over-come the defects of premature and low convergence precision of the standard GSA, has better optimization preci-sion and global convergence performance, and can solve some complex function optimization problems.