Evolutionary Many-Objective Optimization Based on Kuhn-Munkres' Algorithm

In this paper, we propose a new multi-objective evolutionary algorithm (MOEA), which transforms a multi-objective optimization problem into a linear assignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and Kuhn-Munkres' (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our search engine, giving rise to the so-called Hungarian Differential Evolution algorithm (HDE). Our proposed approach is compared with respect to a MOEA based on decomposition (MOEA/D) and with respect to an indicator-based MOEA (the S metric selection Evolutionary Multi-Objective Algorithm, SMS-EMOA) using several test problems (taken from the specialized literature) having from two to ten objective functions. Our preliminary experimental results indicate that our proposed HDE outperforms MOEA/D and is competitive with respect to SMS-EMOA, but at a significantly lower computational cost.