A Parallel Multiple Reference Point Approach for Multi-objective Optimization

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approx-imating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be im-plemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper. Key words: multiple objective programming, parallel computing, multiple reference point approach, evolutionary computations, bi-objective flow-shop schedulin