This paper presents two new approaches for transforming a single-objective problem into a multi-objective problem. The first approach is based on relaxation of the constraints of the problem and the other is based on the addition of noise to the objective value or decision variable. Intuitively, these approaches provide more freedom to explore and a reduced likelihood of becoming trapped in local optima.Through numerical examples, we showed that the multi-objective versions produced by relaxing constraints can provide good results and that using the addition of noise can obtain better solutions when the function is multimodal and separable.
['Shinya Watanabe', 'Kazutoshi Sakakibara']
The effectiveness of multiobjective optimizer in single-objective optimization enviroment