  The application of evolutionary algorithms (EAs) to multi-objective
optimization problems has been widespread. However, the EA research community
has not paid much attention to large-scale multi-objective optimization
problems arising from real-world applications. Especially, Food-Energy-Water
systems are intricately linked among food, energy and water that impact each
other. They usually involve a huge number of decision variables and many
conflicting objectives to be optimized. Solving their related optimization
problems is essentially important to sustain the high-quality life of human
beings. Their solution space size expands exponentially with the number of
decision variables. Searching in such a vast space is challenging because of
such large numbers of decision variables and objective functions. In recent
years, a number of large-scale many-objectives optimization evolutionary
algorithms have been proposed. In this paper, we solve a Food-Energy-Water
optimization problem by using the state-of-art intelligent optimization methods
and compare their performance. Our results conclude that the algorithm based on
an inverse model outperforms the others. This work should be highly useful for
practitioners to select the most suitable method for their particular
large-scale engineering optimization problems.
