MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Ju Chen, Yuan Gao, Mohd Shareduwan Mohd Kasihmuddin (), Chengfeng Zheng, Nurul Atiqah Romli, Mohd. Asyraf Mansor, Nur Ezlin Zamri and Chuanbiao When ()
Additional contact information
Ju Chen: School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China
Yuan Gao: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Mohd Shareduwan Mohd Kasihmuddin: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Chengfeng Zheng: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Nurul Atiqah Romli: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Mohd. Asyraf Mansor: School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia
Nur Ezlin Zamri: School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia
Chuanbiao When: School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610000, China

Mathematics, 2024, vol. 12, issue 5, 1-40

Abstract: The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

Keywords: artificial neural networks; mutation tabu search; satisfiability logic; metaheuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/5/721/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/5/721/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:5:p:721-:d:1348386

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().