The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization

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 [24]. We compare the performance of this new algorithm against two algorithms from the literature (Gavanelli [11], Lukasiewycz et al. [18], Laumanns et al. [17]) on three micro-benchmark problems (n-Queens, nRooks, 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.