Guidance in evolutionary multi-objective optimization

Many real world design problems involve multiple, usually conflicting optimization criteria. Often, it is very difficult to weight the criteria exactly before alternatives are known. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to explore the complete set of non-dominated solutions, which then allows the user to choose among many alternatives. However, although it is very difficult to exactly define the weighting of different optimization criteria, usually the user has some notion as to what range of weightings might be reasonable. In this paper, we present a novel, simple, and intuitive way to integrate the user's preference into the evolutionary algorithm by allowing to define linear maximum and minimum trade-off functions. On a number of test problems we show that the proposed algorithm efficiently guides the population towards the interesting region, allowing a faster convergence and a better coverage of this area of the Pareto optimal front. (C) 2001 Elsevier Science Ltd. All rights reserved.