Weighted sum based approaches to multiobjective optimization is computationally very efficient. However,they have two main weakness: 1) Only one Pareto solution can be obtained in one run 2) The solutions in the concave part of the Pareto front cannot be obtained. This paper proposes a new theory on multiobjective optimization using weighted aggregation approach. Based on this theory, a method called evolutionary dynamic weighted aggregation (EDWA) is developed. The method is able to obtain the Pareto set in one run, no matter whether the Pareto is convex or concave. Therefore, the paper has overcome the two main weaknesses of weighted aggregation approaches. The proposed method has shown to be very efficient and effective on several MOO test functions.
Citation
To be published in "Genetic and Evolutionary Computation Conference", San Francisco, July 2001
Article
View Dynamic Weighted Aggregation for Evolutionary Multiobjective Optimization