Evolutionary Chimp Optimization Algorithm (ECHOA)

Optimizing Sensor Deployment in Table Tennis Officiating: Evolutionary Nature-Inspired Optimization Algorithm for Enhanced Precision

While accuracy is essential when officiating table tennis, human judgment frequently leads to subjective inconsistencies. Our research suggests a novel solution to this problem: adding self-powered acceleration sensors to the referee system to improve ball collision detection accuracy. The Evolutionary Chimp Optimization Algorithm (ECHOA), at the heart of our technique, maximizes sensor node placement with hitherto unheard-of efficiency. ECHOA has a two-pronged technique, making it easier for underachievers to explore and encouraging variety among high achievers, striking a careful balance between discovery and exploitation. In this thorough analysis, we contrast ECHOA with seven other algorithms, such as the Decoupling-Assisted Evolutionary/Metaheuristic Framework (DAEMF), Evolutionary Multi-Objective Seagull Optimization Algorithm (EMOSOA), and variants of the Chimp Optimization Algorithm (CHOA). According to the results, ECHOA may achieve an accuracy level in collision detection with an error margin of less than 3.5 mm by reducing the number of sensors needed from 60 to 49. This paper introduces ECHOA as a novel instrument for intelligent system sensor network optimization. Our solution closes a critical gap in sports technology by greatly improving the objectivity and accuracy of table tennis refereeing, and it sets a new standard for technological integration in sports officiating.