Abstract
Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.
Highlights
Artificial Neural Networks (ANNs) have emerged as a powerful computational model, developed by modelling the biological brain processing information into systematic procedures of mathematical formulation
The Hopfield Neural Network (HNN)-RAN2SAT models were implemented in a systematic procedure, as shown in Figure 1, where the difference is the learning algorithm deployed during the learning phase
The main reason is that the optimization layers in Election Algorithm (EA) have a better partition in solution spaces, meaning EpRAN2SAT = 0 can be achieved in fewer iterations
Summary
Artificial Neural Networks (ANNs) have emerged as a powerful computational model, developed by modelling the biological brain processing information into systematic procedures of mathematical formulation. This work implemented a logical rule into the standard HNN by utilizing the relationship of the cost function and the energy function. In several studies [21,22,23], metaheuristics algorithms were reported to compliment ANN in solving optimization problems. The main challenge in finding a suitable metaheuristic for Satisfiability representation is the structure of the logical rule. The whole process is governed by the campaign process by improving the eligibility of the candidates (solutions of the constrained optimization problem) [28] This algorithm combines the capability of the local search in a partitioned search space. We will adopt EA as the learning algorithm in an HNN to generate global minimum solutions for Random k Satisfiability (RANkSAT). We investigated the RAN2SAT for the and case of k ≤ 2
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
