Intelligent Decision Making In Artificial Intelligence System Using Moth Flame Optimization Algorithm

Intelligent decision-making plays a crucial role in artificial intelligence systems, as it enables effective problem-solving and resource allocation. To enhance the decision-making process, this paper proposes the utilization of the Moth Flame Optimization (MFO) algorithm, a metaheuristic optimization technique inspired by the behavior of moths. The MFO algorithm simulates the attraction of moths to light sources and their interaction with other moths, providing a novel approach for intelligent decision-making. This study explores the application of the MFO algorithm in optimizing decision-making parameters, variables, or features within artificial intelligence systems. The algorithm’s iterative process updates the positions of moths based on predefined rules, taking into account the moths’ attraction to light and movement towards other moths. By effectively exploring the search space and converging towards the optimal solution, the MFO algorithm enhances the efficiency and effectiveness of decision-making algorithms in artificial intelligence systems. Through experimentation and evaluation, this paper demonstrates the applicability and benefits of the MFO algorithm in improving decision-making performance in various domains, such as feature selection, image processing, data clustering, and neural network training. The results highlight the simplicity, robustness, and versatility of the MFO algorithm, positioning it as a valuable tool for intelligent decision-making in artificial intelligence systems.