Temperature and Humidity Optimization of Smart Greenhouses: Comparison Between Simulated Annealing and Genetic Algorithm

The greenhouse industry has recently seen substantial developments and rising expenses, which makes production optimization necessary. They strive to reduce costs while maintaining ideal conditions because temperature significantly impacts plant development. Therefore, a comparison between the Simulated Annealing (SA) and Genetic Algorithm (GA) based on digital simulation was made to assess how well each algorithm optimized temperature and humidity. This paper used a comparative analysis method to compare these two algorithms. The results show that GA outperforms SA, making it a better solution for complex optimization issues. The main objective of this paper is to analyze genetic and simulated techniques for the most efficient temperature and humidity control in an intelligent greenhouse. These advancements in optimization algorithms promote increased productivity, efficiency, and decision-making in greenhouses, ultimately advancing societal goals.