Parameter Sensitivity Analysis of Social Spider Algorithm
James J.Q. Yu, Victor O.K. Li
TL;DR
This paper systematically analyzes the parameter sensitivity of the Social Spider Algorithm (SSA) across various functions, providing statistically significant insights to guide parameter tuning and improve convergence speed.
Contribution
It offers a comprehensive sensitivity analysis of SSA's control parameters using advanced statistical tests, aiding future parameter tuning efforts.
Findings
Identifies optimal parameter settings for SSA
Shows how parameters affect convergence speed
Provides statistically significant conclusions on parameter effects
Abstract
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.
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Evolutionary Algorithms and Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings