This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the performance of this new algorithm against two algorithms from the literature [Gavanelli 2002, Lukasiewycz et alia 2007, Laumanns et alia 2006]) on three micro-benchmark problems (n-Queens, n-Rooks, and knapsack) and on two aerospace case studies. Results indicate that the new algorithm is better for the kinds of many-objective problems that our aerospace collaborators are interested in solving. The new algorithm returns Pareto-optimal solutions as it computes.
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