Dual Guidance in Evolutionary Multi-objective Optimization by Localization

In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front using the guided dominance technique in the objective space. With this dual guidance, we can easily guide spheres towards different parts of the Pareto front while also exploring the decision space efficiently.