Pareto Local Optima of Multiobjective NK-Landscapes with Correlated Objectives
This work addresses the impact of local optima on metaheuristic performance in multiobjective combinatorial optimization, but it is incremental as it extends existing single-objective analysis to the multiobjective case.
The paper analyzes how problem dimension, non-linearity, number of objectives, and correlation affect the number of Pareto local optima in multiobjective NK-landscapes, extending single-objective local optima concepts to multiobjective optimization.
In this paper, we conduct a fitness landscape analysis for multiobjective combinatorial optimization, based on the local optima of multiobjective NK-landscapes with objective correlation. In single-objective optimization, it has become clear that local optima have a strong impact on the performance of metaheuristics. Here, we propose an extension to the multiobjective case, based on the Pareto dominance. We study the co-influence of the problem dimension, the degree of non-linearity, the number of objectives and the correlation degree between objective functions on the number of Pareto local optima.