Extra dimension algorithm: a breakthrough for optimization and enhancing DNN efficiency

Abstract

Proposing an efficient meta-heuristic to improve the inputs of a trainer in deep neural network (DNNs) is significant. According to the Kaluza’s theory, there exists an extra dimension in the universe. This paper proposes a novel algorithm, extra dimension algorithm (EDA), which is simulated based on this theory. The proposed algorithm utilizes the extra dimension to evaluate the current region of solutions and determine the best direction to follow for the next step of the process. Finally, EDA is used to improve inputs of DNN in the process of solving optimization test problems. The same DNN with and without EDA is used to solve extensive optimization problems, including energy-related tasks. The efficiency of EDA in DNN is assessed by solving some test problems in references, the feasibility and efficiency of solutions, within a suitable number of iterations are demonstrated according to the results. The contributions of this paper are as follows: (1) Introduction of the EDA based on Kaluza’s theory. (2) Application of EDA to enhance the performance of DNNs. (3) Demonstration of EDA’s effectiveness in solving complex optimization problems. (4) Comprehensive evaluation of EDA’s impact on energy optimization problems and other test cases. (5) EDA achieved an average improvement of 15% in optimization accuracy and reduced convergence time compared to the best-performing alternatives.