Innovative Synergistic Genetic Algorithm and Particle Swarm Optimization for Scheduling Optimization in Mobile Robots

Abstract

Autonomous Mobile Robots (AMRs) are crucial in modern manufacturing for automating material handling and transportation. However, optimizing their task scheduling is challenging due to conflicting objectives like minimizing makespan and reducing energy consumption. Traditional algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) often yield suboptimal results. This study proposes an innovative Synergistic GA-PSO algorithm that combines the exploratory capabilities of GA with the fast convergence of PSO. Experiments conducted in MATLAB demonstrate that the Synergistic GA-PSO algorithm consistently outperforms GA, PSO, and ACO, especially in complex environments, by enhancing scheduling accuracy, reducing idle intervals, and lowering energy consumption.