Approximating the irregularly shaped Pareto front of multi-objective reservoir flood control operation problem

Abstract

Decomposition based multi-objective evolutionary algorithm (MOEA/D) has been proved to be effective on multi-objective optimization problems. However, it fails to achieve satisfactory coverage and uniformity on problems with irregularly shaped Pareto fronts, like the reservoir flood control operation (RFCO) problem. To enhance the performance of MOEA/D on the real-world RFCO problem, a Pareto front relevant (PFR) decomposition method is developed in this paper. Different front the decomposition method in the original MOEA/D which is based on a unique reference point (i.e. the estimated ideal point), the PFR decomposition method uses a set of reference points which are uniformly sampled from the fitting model of the obtained Pareto front. As a result, the PFR decomposition method can provide more flexible adaptation to the Pareto front shapes of the target problems. Experimental studies on benchmark problems and typical RFCO problems at Ankang reservoir have illustrated that the proposed PFR decomposition method significantly improves the adaptivity of MOEA/D to the complex Pareto front shape of the RFCO problem and performs better both in terms of coverage and uniformity.