comparison of evolutionary multiobjective optimization with rference solution-based single-objective approach
In this paper, we demonstrate advantages and disadvantages of an evolutionary multiobjective optimization (EMO) approach in comparison with a reference solution-based single-objective approach through computational experiments on multiobjective 0/1 knapsack problems. The main characteristic feature of the EMO approach is that no a priori information about the decision maker's preference is assumed. The EMO approach tries to find well-distributed trade-off solutions with a wide range of objective values as many as possible. A final solution is supposed to be chosen from the obtained trade-off solutions by the decision maker. On the other hand, the reference solution-based approach utilizes the information about the decision maker's preference in the form of a reference solution. We examine whether the EMO approach can find good trade-off solutions close to an arbitrarily given reference solution. Experimental results show that good solutions are not always obtained by the EMO approach. We also examine where the reference solution-based approach can find many trade-off solutions around the given reference solution. Experimental results show that many trade-off solutions can not be obtained even when an archive population of non-dominated solutions is stored in the reference solution-based approach. Based on these observations, we suggest a hybrid approach.
reference solutions decision maker's preference nsga-ii genetic algorithms diversity-preserving strategies