Abstract: The spider monkey algorithm（Spider Monkey Optimization, SMO） is a swarm intelligence optimization algorithm inspired by simulating the foraging behavior of spider monkeys. In order to enhance the local search  performance of SMO, an algorithm based on dynamic self-adaptive inertia weight（DWSMO） is proposed. By introducing the value of the objective function into the inertia weight, the inertia weight can change dynamically with the objective function value. This reduces the changing blindness of the inertia weight and effectively balances the algorithm’s global exploration and local exploitation ability. The improved spider monkey algorithm is tested on function optimization problems. The simulation results show that the new algorithm can effectively improve the function optimization accuracy and the convergence speed, and has a strong stability.

Key words: spider monkey optimization, self-adaptive, dynamic inertia weight, function optimization

摘要： 蜘蛛猴算法（Spider Monkey Optimization，SMO）是受蜘蛛猴觅食行为启发提出的一种群集智能优化算法，为增强蜘蛛猴算法的局部搜索性能，提出一种基于动态自适应惯性权重的SMO算法（DWSMO）。通过在惯性权重中引入目标函数值，使得惯性权重随着目标函数值的变化而动态改变，从而减少惯性权重变化的盲目性，有效平衡算法的全局探索能力以及局部开发能力。将改进的蜘蛛猴算法在函数优化问题上进行测试，仿真实验结果表明，改进的蜘蛛猴算法可有效提高函数寻优精度，加快收敛速度，且具有较强的稳定性。

关键词: 蜘蛛猴算法, 自适应, 动态惯性权重, 函数优化