Interactive cone contraction for evolutionary mutliple objective optimization

Abstract

We present a new interactive evolutionary algorithm for Multiple Objective Optimization (MOO) which combines the NSGA-II method with a cone contraction method. It requires the Decision Maker (DM) to provide preference information in form of a reference point and pairwise comparisons of solutions from a current population. This information is represented with a compatible Achievement Scalarizing Function (ASF) which is used to guide the evolutionary search towards the most preferred region of the Pareto front. The performance of the proposed algorithm is illustrated on a set of benchmark problems. The experimental results confirm its ability to converge quickly to the DM’s most preferred region. Its competitive advantage over the state-of-the-art method, called NEMO-0, is increasing when the DM provides a richer preference information composed of a greater number of pairwise comparisons of solutions.