关键词: 蜘蛛猴算法, 自适应, 动态惯性权重, 函数优化

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