How Effective is Spotted Hyena Optimizer for Training Multilayer Perceptrons
Nibedan Panda1, Santosh Kumar Majhi2
1Nibedan Panda, Department of Computer Science & Engineering, VSSUT, Burla, Odisha, India.
2Dr. Santosh Kumar Majhi, Department of Computer Science & Engineering, VSSUT, Burla, Odisha, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 11 March 2019 | Manuscript published on 30 July 2019 | PP: 4915-4927 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3736078219/19©BEIESP | DOI: 10.35940/ijrte.B3736.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Classification, MLP, SHO, DE, GA, PSO, GWO, SSA, ANN
Scope of the Article: Classification