Identifying preferred solutions in multiobjective combinatorial optimization problems

We develop an evolutionary algorithm for multi-objective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision maker. We test the performance of the algorithm on the multi-objective knapsack and multi-objective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the preferred regions.