Parameter Sensitivity Analysis of Social Spider Algorithm
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.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Social Spider Algorithm for Global Optimization
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 exa…
global-optimizationA Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Economic Load Dispatch (ELD) is one of the essential components in power system control and operation. Although conventional ELD formulation can be solved using mathematical programming techniques, modern power system in…
A swarm optimization algorithm inspired in the behavior of the social-spider
Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization pro…
Evolutionary AlgorithmsThe Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
Incremental Expectation Maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each iteration. Nevertheless, these algorithms all…
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics
Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, the…
Neural Architecture Search