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A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

Mathematics (Basel), 2022-06, Vol.10 (12), p.1963 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2227-7390 ;EISSN: 2227-7390 ;DOI: 10.3390/math10121963

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Title:
A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network
Author: Karim, Syed Anayet ; Kasihmuddin, Mohd Shareduwan Mohd ; Sathasivam, Saratha ; Mansor, Mohd. Asyraf ; Jamaludin, Siti Zulaikha Mohd ; Amin, Md Rabiol
Subjects: Accuracy ; Combinatorial analysis ; Discrete Hopfield Neural Network ; election algorithm ; Elections ; evolutionary algorithm ; Evolutionary algorithms ; Exploitation ; Genetic algorithms ; Heuristic methods ; hybrid election algorithm ; hybridized algorithm ; Logic programming ; Machine learning ; Multiple objective ; Neural networks ; Optimization ; Performance enhancement ; Performance measurement ; random k satisfiability ; Search algorithms ; Statistical analysis ; Storage capacity ; Swarm intelligence
Is Part Of: Mathematics (Basel), 2022-06, Vol.10 (12), p.1963
Description: Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 2227-7390
EISSN: 2227-7390
DOI: 10.3390/math10121963
Source: Coronavirus Research Database
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

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