Multi-objective Evolutionary Algorithms : Data Structures, Convergence, and Diversity

Many real-world optimization problems consist of several conflicting objectives, the solutions of which is a set of trade-offs called the Pareto-optimal set. During the last decade, Evolutionary Algorithms (EAs) have been utilized to find an approximation of the Pareto-optimal set. However, the approximation set must possess solutions with high convergence towards the Pareto-optimal set and hold a good diversity in order to demonstrate a good approximation.The subject of this thesis is to improve the existing Multi-Objective Evolutionary Algorithms (MOEAs) and to develop new techniques in order to achieve approximated sets with high convergence and diversity in low computational time.