Having developed multi-objective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multi-objective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a

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... al EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point based many-objective NSGA-II (we call it NSGA-III) that emphasizes population members which are non-dominated yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems having two to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this study. This paper presents results on unconstrained problems and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.