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针对社会蜘蛛优化算法(social spider optimization algorithm,SSA)在寻优过程中步长固定且蜘蛛种群间因吸引力降低导致收敛速度慢且迭代后期计算精度低的缺陷,提出了一种改进型社会蜘蛛优化算法(modified social spider optimization algorithm,MSSA).算法采用自适应方法使寻优步长在迭代过程中自适应变化,提高了其收敛性能.引入偏好随机游动机制进一步强化算法的局部开发能力.典型函数的测试表明,MSSA的收敛性能较标准SSA及其它改进的群智能算法在收敛速度及精度方面具有明显优势.
In order to solve the shortcomings of the social spider optimization algorithm (SSA), which has a fixed step in the optimization process and the spider population reduces its attractiveness due to the slow convergence rate and low accuracy in the late iteration, an improved social spider The proposed algorithm adopts adaptive social spatio-temporal optimization algorithm (MSSA), which adaptively changes the optimal step size during iterative process and improves its convergence performance. Tests of typical functions show that the convergence performance of MSSA has obvious advantages in convergence speed and accuracy over standard SSA and other improved swarm intelligence algorithms.