余剑峤
James Jianqiao Yu
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讲师(助理教授)

计算机科学系

约克大学

英国约克 YO10 5GH CSE/139

jqyu(at)ieee.org Google Scholar
A Social Spider Algorithm for Global Optimization

作者
James J.Q. Yu and Victor O.K. Li

发表
Applied Soft Computing, Volume 30, May 2015, Pages 614--627

摘要
The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The Social Spider Algorithm is evaluated by a series of widely-used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.