How Effective is Spotted Hyena Optimizer for Training Multilayer Perceptrons

Abstract

This paper focuses on training multilayer perceptron (MLP) using a recently proposed meta-heuristic algorithm termed as Spotted Hyena Optimizer (SHO). To test the efficacy of the said algorithm fifteen standard datasets are used. At the same time the result of the proposed method is examined by some popular heuristic training algorithms such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA) and Grey Wolf Optimization algorithm (GWO). Final result shows that SHO successfully avoids the local minima trap problem, simultaneously showing higher accuracy in classification as compared to other meta-heuristic methods. The statistical significance of the proposed SHO-MLP has been verified by deploying the Friedman & Holm’s test. It has been observed that the SHO-MLP is giving promisingly better result than other compared method for training MLP.

Otimização via Hiena-Malhada aplicado ao Planejamento da Expansão da Transmissão Considerando Contingência e Geração de Energia Eólica

“…Em (Panda, 2018), o SHO foi utilizado para o treinamento de redes neurais e obteve desempenho superior a metaheurísticas populares como AG, PSO e GWO.…”