Extreme Learning Machine Classification Method based on Twin Strategy Salp Swarm Algorithm


  • Meiling Shang
  • Rongguo Qu
  • Deqing Ji
  • Zhenxing Yu
  • Qinwei Fan




Extreme Learning Machine, Salp Swarm Algorithm, Multi-Strategy, Inertia Weights, Classification Problems


Extreme Learning Machines (ELM) are a type of Single Hidden Layer Feedforward Neural Network (SLFN). In recent years, they have attracted attention for their powerful approximation ability and fast learning speed. Compared with traditional neural network algorithms, ELM have the advantages of simple structure, fast learning speed, and good generalization performance. However, since the input weights and biases of ELM are randomly generated, there may be some suboptimal or unnecessary input weights and biases. In addition, ELM may require more hidden nodes, which may slow down its response to unknown test data. To address these issues, a twin strategy Salp Swarm Algorithm (TSSA) is proposed to increase the population diversity to improve the convergence speed of the algorithm through chaotic initialization, while shock inertia weights and learning paradigms are introduced into the follower position updating to enhance the stochasticity of the particles. Classification tests are conducted on six datasets using the proposed TSSA, and the experimental results show that the proposed TSSA-ELM has higher accuracy and better generalization performance than some existing ELM variants.


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How to Cite

Shang, M., Qu, R., Ji, D., Yu, Z., & Fan, Q. (2024). Extreme Learning Machine Classification Method based on Twin Strategy Salp Swarm Algorithm. Frontiers in Computing and Intelligent Systems, 7(1), 64-68. https://doi.org/10.54097/pv9cv641