Optimization and simulation of intelligent aquaponics system using evolutionary algorithms

Aquaponics represents an eco-friendly and resource-efficient approach to food production by integrating aquaculture and hydroponic farming in a synergistic cycle. In this system, the waste generated by fish serves as a nutrient source for plants, which in turn contribute to water filtration and purification. This paper introduces a smart aquaponics model, enhanced with evolutionary optimization techniques and supported by IoT-based monitoring. An ESP32 microcontroller is deployed as the central processing unit, connected to various sensors including DHT12 for temperature and humidity, TDS for measuring dissolved solids, and pH sensors to track water quality parameters. Sensor readings are continuously transmitted to a cloud-based platform, allowing for remote access, data analytics, and adaptive system control. This study evaluates the performance of three optimization algorithms, Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) in enhancing critical parameters such as plant growth efficiency and overall system robustness. The findings emphasize the practical value of integrating IoT and computational intelligence in advancing sustainable agriculture through smart aquaponics systems.