Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

• Published in: Journal of physics. Conference series Vol. 1821; no. 1; p. 12038
• Main Authors: Mansor, Mohd. Asyraf, Kasihmuddin, Mohd Shareduwan Mohd, Sathasivam, Saratha
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2021
• Subjects: Algorithms; Boolean algebra; Cost function; Heuristic methods; Iterative methods; Machine learning; Neural networks; Optimization; Optimization algorithms; Performance evaluation; Performance measurement; Physics; Representations
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1821/1/012038

An optimal learning algorithm contributes to the quality of the neuron states in the form of 3 Satisfiability logical representation during the retrieval phase of the Discrete Hopfield Neural Network. Based on that basis, we proposed a modified bipolar Grey Wolf Optimization algorithm with a Discrete Hopfield Neural Network for Boolean 3 Satisfiability analysis by manipulating the different levels of complexities. This work concerns the improvement in the learning phase which requires a robust iterative metaheuristic algorithm in minimizing the cost function of 3 Satisfiability logical representation with less iteration. Under some reasonable conditions, the proposed hybrid network will be assessed by employing several performance measures, in terms of learning errors, minimum energy evaluations, variability, and similarity analysis. To verify the compatibility of the Grey Wolf Optimization algorithm as a learning paradigm, the comparison was made with the hybrid model with an Exhaustive search. Thus, the results proved the capability of the proposed learning algorithm in optimizing the learning and generating global minimum solutions for 3 Satisfiability logic based on the analysis obtained via various performance metrics evaluation.