Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Version 1 : Received: 29 January 2020 / Approved: 30 January 2020 / Online: 30 January 2020 (11:46:31 CET)

A peer-reviewed article of this Preprint also exists.

Sathasivam, S.; Mansor, M.A.; Kasihmuddin, M.S.M.; Abubakar, H. Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. Processes 2020, 8, 568. Sathasivam, S.; Mansor, M.A.; Kasihmuddin, M.S.M.; Abubakar, H. Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. Processes 2020, 8, 568.

Journal reference: Processes 2020, 8, 568
DOI: 10.3390/pr8050568

Abstract

Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

Subject Areas

Hopfield Neural Networks; Election Algorithm; Imperialistic Competitive Algorithm; Exhaustive Search; Random Satisfiability; Logic Programming

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