In recent years, the development of multi-objective optimization techniques for simultaneously optimizing multiple and conflicting objectives have received wide attention in the literature. In this paper, three algorithms for generating the Pareto domain were studied for their efficiency to generate a well-defined Pareto domain as a first step in the development of a multi-objective optimization strategy. Twelve standard test cases, which have been used frequently in the literature, were considered along with two engineering problems, namely, the determination of optimum operating conditions for the production of gluconic acid and the determination of optimal tuning parameters for a PI controller. These multi-objective optimization problems were selected in order to evaluate the robustness and versatility of the algorithms studied. The results of three of these test cases and both engineering problems are presented. The results identify a robust optimization strategy that generates a Pareto domain using a dual population evolutionary algorithm and classifies it using net flow, a technique that incorporates the knowledge of an expert into the optimization routine. (c) 2006 Elsevier Ltd. All rights reserved.