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
Parameter Sensitivity Analysis of Social Spider Algorithm

作者
James J.Q. Yu and Victor O.K. Li

发表
Proc. IEEE Congress on Evolutionary Computation, Sendai, Japan, May 2015

摘要
Social Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems. This work systematically benchmarks SSA on a suite of 11 functions with different control parameters. We conduct parameter sensitivity analysis of SSA using advanced non-parametric statistical tests to generate statistically significant conclusion on the best performing parameter settings. The conclusion can be adopted in future work to reduce the effort in parameter tuning. In addition, we perform a success rate test to reveal the impact of the control parameters on the convergence speed of the algorithm.