Multiple objective optimization of LED lighting system design using genetic algorithm

In order to maximize the advantages of LED lighting systems for controlled environment agriculture (CEA), several considerations must be taken into account such as the achievement of required daily light integral (DLI), uniform light distribution over the plant growing area, and minimize the investment and operating costs associated with the lighting system. This study aims to apply the multiple objective optimization of genetic algorithm in designing a lighting system that meets the mentioned objectives. The optimization variables, number of bits per variable and maximum number of iterations are fixed parameters tuned to the requirements of this application and the population size, mutation rate, and selection rate are genetic parameters for explorations. Results of the algorithm suggest the use of a number of LED lamps that is 31.25% lower than the maximum number of lamps that may be used in the plant growing area and, consequently, reduce the investment and operating costs while maintaining the required light integral capacity and uniformity. This and other studies that aim to develop and optimize LED lighting systems open more possibilities and promote the technology for controlled environment. Moreover, control and optimization of agricultural practices can lead to better plant quality and production even on locations and periods that they do not usually grow.