e-ISSN 2231-8542
ISSN 1511-3701

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Metaheuristics Approach for Maximum k Satisfiability in Restricted Neural Symbolic Integration

Saratha Sathasivam, Mustafa Mamat, Mohd Shareduwan Mohd Kasihmuddin and Mohd. Asyraf Mansor

Pertanika Journal of Tropical Agricultural Science, Volume 28, Issue 2, April 2020

Keywords: Artificial bee colony, exhaustive search method, genetic algorithm, Hopfield neural network, maximum k satisfiability

Published on: 15 April 2020

Maximum k Satisfiability logical rule (MAX-kSAT) is a language that bridges real life application to neural network optimization. MAX-kSAT is an interesting paradigm because the outcome of this logical rule is always negative/false. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when the logical rule is embedded in HNN. Increasing the storage capacity during the learning phase of HNN has been a challenging problem for most neural network researchers. Development of Metaheuristics algorithms has been crucial in optimizing the learning phase of Neural Network. The most celebrated metaheuristics model is Genetic Algorithm (GA). GA consists of several important operators that emphasize on solution improvement. Although GA has been reported to optimize logic programming in HNN, the learning complexity increases as the number of clauses increases. GA is more likely to be trapped in suboptimal fitness as the number of clauses increases. In this paper, metaheuristic algorithm namely Artificial Bee Colony (ABC) were proposed in learning MAX-kSAT programming. ABC is swarm-based metaheuristics that capitalized the capability of Employed Bee, Onlooker Bee, and Scout Bee. To this end, all the learning models were tested in a new restricted learning environment. Experimental results obtained from the computer simulation demonstrate the effectiveness of ABC in modelling MAX-kSAT.

ISSN 1511-3701

e-ISSN 2231-8542

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