02123nas a2200253   4500000000100000000000100001008004100002260001200043653001500055653002600070653001900096653002100115653001000136100004100146700002700187700003100214700002300245245011200268856008100380300001200461490000600473520137600479022001401855       2021                            d  c06/202110aBee Colony10aRadial Basis Function10aNeural Network10a2 Satisfiability10aLogic1 aMohd Shareduwan Bin Mohd Kasihmuddin1 aMohd Asyraf Bin Mansor1 aShehab Abdulhabib Alzaeemi1 aSaratha Sathasivam00aSatisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_17.pdf  a164-1730 v63 aRadial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm.   a1989-1660