计算机工程与应用2019，Vol.55Issue(14)：40-47,8.DOI:10.3778/j.issn.1002-8331.1804-0025
含有动态自适应惯性权重的蜘蛛猴优化算法
Spider Monkey Optimization Algorithm with Dynamic Self-Adaptive Inertia Weight
摘要
Abstract
The spider monkey algorithm(Spider Monkey Optimization, SMO)is a swarm intelligence optimization algo-rithm 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 simula-tion results show that the new algorithm can effectively improve the function optimization accuracy and the convergence speed, and has a strong stability.关键词
蜘蛛猴算法/自适应/动态惯性权重/函数优化Key words
spider monkey optimization/self-adaptive/dynamic inertia weight/function optimization分类
信息技术与安全科学引用本文复制引用
党婷婷,林丹..含有动态自适应惯性权重的蜘蛛猴优化算法[J].计算机工程与应用,2019,55(14):40-47,8.
