Bioinspiration-based deep learning algorithm

Abstract

This paper presents the Infection Susceptible Artificial Intelligence Optimization Model (SIMO, susceptible-infected- removed model optimizer), an innovative learned heuristic inspired by biological systems and Deep Learning (DL) techniques. The SIMO optimization algorithm estimates the susceptibility of the population to infection, active infections and the recovering population at any point in time, inspired by the epidemiological partition model with Infection-Sensitive Artificial Intelligence. SIMO integrates the IA method into the initialisation method and parameter tuning components to improve the search process, so that it can exhibit intelligent and autonomous behaviour. The integration of the IO facilitates the generation of initial solutions based on neural models, which allows the algorithm to be guided towards efficient, effective and robust search results. This approach improves the performance of the algorithm by obtaining high-level solutions, allowing it to converge faster, increasing its robustness and reducing its computational requirements. Two datasets from the 2017 IEEE Congress on Evolutionary Computing (CEC 2017) benchmarking functions are used to validate the effectiveness of the SIMO algorithm and the experimental results are compared with innovative algorithms. Detailed comparisons show that SIMO outperforms many similar models, offering high performance solutions using fewer control parameters. Furthermore, the performance of SIMO is adapted to real-life problems. The results clearly show that integrating the learning process into SIMO provides superior accuracy and computational efficiency compared to other optimization approaches in the existing literature.