Objective-based survival individual enhancement in the chimp optimization algorithm for the profit prediction using financial accounting information system

Abstract

This paper develops an innovative Objective-based Survival Individual Enhancement approach for the Chimp Optimization Algorithm (OSIE-CHOA) designed to enhance financial accounting profit prediction using information systems. The OSIE-CHOA focuses on improving the search process by simultaneously elevating the fitness of under-performing individuals within a population and strengthening the diversity among the top-performing ones. Within the OSIE-CHOA, we identify the four most promising chimps during each iteration. Subsequently, half of the highest-performing chimps are selected for elimination and repositioning around these fortunate individuals, with an equal probability assigned to each chimp. According to the experimental findings, it is clearly seen that OSIE-CHOA considerably enhances prediction accuracy, allowing a decrease in the root mean square error (RMSE) by 15% and the mean absolute error (MAE) by 18% compared to the traditional CHOA. Moreover, OSIE-CHOA shows a convergence rate that is 20% higher, which makes it a good and efficient tool for financial analysts who require accurate and reliable profit forecasting. By facilitating the optimization of profit prediction models, OSIE-CHOA leads to the improvement of decision-making within the context of financial accounting information systems.