Effective anytime algorithm for multiobjective combinatorial optimization problems

Abstract In multiobjective optimization, the result of an optimization algorithm is a set efficient solutions from which decision maker selects one. It common that not all can be computed in short time and search has to stopped prematurely analyze found so far. A are well-spread objective space preferred provide with great variety solutions. However, just few exact algorithms literature exist ability such at any moment: we call them anytime algorithms. We propose new for combinatorial combining three novel ideas enhance behavior. compare proposed those state-of-the-art using 480 instances different well-known benchmarks four performance measures: overall non-dominated vector generation ratio, hypervolume, general spread additive epsilon indicator. comprehensive experimental study reveals our proposal outperforms previous most instances.