USING DIVERSITY TO GUIDE THE SEARCH IN MULTI-OBJECTIVE OPTIMIZATION
Dario Landa-Silva, E. K. Burke · Advances in natural computation · 2004
The overall aim in multi-objective optimization is to aid the decisionmaking process when tackling multi-criteria optimization problems. In an a posteriori approach, the strategy is to produce a set of nondominated solutions that represent a good approximation to the Pareto optimal front so that the decision-makers can select the most appropriate solution. In this paper we propose the use of diversity measures to guide the search and hence, to enhance the performance of the multi-objective search algorithm. We propose the use of diversity measures to guide the search in two different ways. First, the diversity in the objective space is used as a helper objective when evaluating candidate solutions. Secondly, the diversity in the solution space is used to choose the most promising strategy to approximate the Pareto optimal front. If the diversity is low, the emphasis is on exploration. If the diversity is high, the emphasis is on exploitation. We carry out our experiments on a two-objective optimization problem, namely space allocation in academic institutions. This is a real-world problem in which the decision-makers want to see a set of alternative diverse solutions in order to compare them and select the most appropriate allocation.