PROJECT TITLE :

An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition

ABSTRACT:

Achieving balance between convergence and variety could be a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical issues involving 2 and 3 objectives, face vital challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-primarily based approaches, for many-objective optimization. Our major purpose is to use the deserves of each dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary method. The performance of our proposed methodology is validated and compared with four state-of-the-art algorithms on a range of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed methodology on all considered test instances. Still, we have a tendency to extend this method to unravel constrained problems having a large variety of objectives. Compared to two other recently proposed constrained optimizers, our proposed methodology shows highly competitive performance on all the constrained optimization issues.