An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition PROJECT TITLE :An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and DecompositionABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Robust stabilisation of power systems with random abrupt changes Can Topic Modeling Shed Light on Climate Extremes?
