ANN Model Identification: Parallel Big Bang Big Crunch Algorithm
Ashima Kalra1, Shakti Kumar2, Sukhbir Singh Walia3
1Ashima Kalra, P.H.D, research scholar, Punjab Technical University, Punjab, India.
2Prof. Shakti Kumar, Director, Panipat Institute of Engineering Technology, Panipat (Haryana), India.
3Dr. Sukhbir Singh Walia, Registrar, Punjab Technical University, Punjab, India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 323-330 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10520789S19/19©BEIESP | DOI: 10.35940/ijitee.I1052.0789S19
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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 proposes a modification to existing big bang big crunch optimization algorithm that uses the concept of more than one population. In this the search begins with all the populations independently in parallel and as the algorithm proceeds the local best of the individual populations interact with global best to avoid local minima. In order to validate the proposed approach the authors have identified two models one from control field namely rapid battery charger and second a rating system for institutes of higher learning and compared its results with simple BB-BC based approach .The author further compared results of the proposed approach with the results of other recent soft computing based algorithms for ANN model identification. The proposed algorithm outperformed all of the other 7 algorithms in terms of MSE and convergence time.
Keywords: Model Identification, ANN (Artificial Neural Network), Big Bang Big Crunch (BB-BC) Optimization, Parallel Big Bang Big Crunch (PBB-BC) Optimization Levenberg-Marquardt Algorithm (LM), Error Back Propagation (EBP), Resilent Prop (RPROP), Particle Swarm Optimization (PSO), ant Colony Optimization(ACO) and Artificial Bee Colony(ABC).
Scope of the Article: Waveform Optimization for Wireless Power Transfers