混合方法优化的自适应引力搜索算法

针对引力搜索算法存在的易早熟收敛、易陷入局部最优、搜索精度有待提高等缺陷，提出一种混合方法优化的自适应引力搜索算法（gravitational search algorithm，GSA）。首先利用Sobol序列初始化种群，增强算法全局搜索能力；其次引入Hamming贴进度计算种群成熟度，判断种群是否早熟；然后引入Logistic混沌对种群作混沌搜索，变异已陷入局部最优的粒子位置；最后基于早熟收敛判断因子改进引力系数，并为粒子位置公式添加收缩因子，促使种群加快脱离局部最优。对9个不同类型的基准测试函数做仿真实验，结果表明新算法能有效改善种群的早熟问题，具备更好的寻优性能。

Adaptive gravitational search algorithm improved by hybrid methods

In order to overcome the shortcomings of premature convergence, trapping in local optimum easily and lower search accuracy of gravitational search algorithm (GSA), an adaptive GSA improved by hybrid methods is proposed. Firstly, sobol sequence is used to initialize the population and enhance the global search ability. Secondly, hamming nearness degree is introduced to calculate the population maturity and judge whether the population is premature. Thirdly, logistic chaos is introduced to search the population chaotically and update the particle which has fallen into the local optimum. Finally, based on the precocious convergence judgment factor, the gravitational coefficient is improved, and the shrinkage factor is added to the particle position formula to accelerate the population departure from the local optimum. The simulation results of nine different types of benchmark functions show that the new algorithm can effectively improve the premature convergence problem and has better optimization performance.