Swarm Intelligence-Based Optimisation and Its Application in Computational Convergence in Electronic Health Records

Abstract

In many different fields, optimization—the search for the optimal solution within predetermined parameters—is essential to solving challenging issues. This study delves into the field of optimisation problems and examines how Swarm Intelligence (SI) approaches might be applied, with a specific emphasis on Particle Swarm Optimisation (PSO). Inspired by biological phenomena like herding, flocking, and swarming in vertebrates, SI provides a novel solution to optimisation problems. Engineering designs, agricultural sciences, manufacturing systems, economics, physical sciences, and pattern recognition are among the fields in which the study examines the landscape of optimisation problems. There has been an explosion in global optimisation algorithms in the last few decades, particularly in nature-inspired meta-heuristics. Neural network techniques, evolutionary algorithms (such as genetic algorithms), and simulated annealing have become more popular as general-purpose algorithms that can be used to solve a variety of issues.