Optimizing Greenhouse Lighting Systems Using Genetic Algorithms

Greenhouse lighting is an essential component of indoor agriculture, providing artificial light that simulates natural light and creates a controlled growth environment. However, optimizing greenhouse lighting is a challenging task due to the need to consider multiple factors, such as crop requirements, daylight exposure, and light intensity, among others. If optimized correctly, greenhouse lighting can be both environmentally friendly and cost-effective, by accelerating plant growth and reducing power consumption and costs. To address this issue, we employed NSGA-II, a genetic algorithm (GA) known for its strong multi-objective optimization capabilities. Genetic algorithms are highly effective in managing complex and nonlinear problems with numerous variables and constraints, outperforming other optimization algorithms that rely on mathematical models or analytical solutions. By using NSGA-II, we were able to determine the optimal number and positioning of light devices required for efficient greenhouse lighting. The results of our study demonstrate that the optimized lighting plan significantly reduces lighting costs while ensuring more even light distribution. The results of this research have significant implications for greenhouse systems, highlighting the potential benefits of applying genetic algorithms to optimize complex and nonlinear systems.