Search Space Optimization for Autonomous Mobile Robots Using Meta-Heuristics

Optimizing search space for autonomous mobile robots is a critical problem that affects their ability to efficiently navigate and perform tasks in various environments. Considering the growing level of complexity of robotic systems, the area of search space for finding optimal solutions can be very large and time-consuming. Meta-heuristics are a class of algorithms designed for exploring large search spaces and locating near-optimal solutions. Their application is one of the strategies and approaches to addressing this challenge. This research paper presents metaheuristic techniques such as the firefly algorithm(FA), particle swarm optimization(PSO), and differential evolution(DE) in search space optimization of autonomous mobile robots. Also, it covers an overview of each algorithm, indicating its benefits and drawbacks and demonstrating how it potentially performs in varied search space environments. The study also looks at how various hyper-parameters influence the way each algorithm performs. The findings indicate that these factors significantly affect the algorithm’s functioning and the most appropriate values can be identified through a parameter-tuning technique. We also discuss some challenges and possibilities for future study in this area. Furthermore, for researchers and practitioners interested in using meta-heuristics to address search space optimization problems for robots or any other search space-related applications this study offers insightful guidance.