A multi-objective niching co-evolutionary algorithm (MNCA) for identifying diverse sets of non-dominated solutions

Authors:
Emily Michelle Zechman;Marcio H. Giacomoni;M. Ehsan Shafiee
Affiliations:
Texas A&M University, College Station, TX, USA;Texas A&M University, College Station, TX, USA;Texas A&M University, College Station, TX, USA
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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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.