Abstract
Many engineering design problems must optimize multiple objectives. While many objectives are explicit and can be mathematically modeled, some goals are subjective and cannot be included in a mathematical model of the optimization problem. A set of alternative Pareto fronts that represent multiple optima for problem solution can be identified to provide insight about the decision space and to provide options and alternatives for decision-making. This paper presents the Multi-objective Niching Co-evolutionary Algorithm (MNCA) that identifies a set of Pareto-optimal solutions which are maximally different in their decision vectors and are located in the same non-inferior regions of the Pareto front. MNCA is demonstrated for a set of multi-modal multi-objective test problems to identify a set of Pareto fronts with maximum difference in decision vectors.
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