Syllabus

Short Course Description

Syllabus: This course will focus on methods for multi-objective optimization and their combination with multi-criteria decision-making techniques. First, classical methods for treating multi-objective problems will be presented, and their deficiencies will be clarified. Next, advanced methods, which are based on Pareto-optimality, will be presented. The major part of the course will be based on evolutionary techniques for optimization problems with and without constraints. Leading algorithms will be presented and compared, such as: NSGA-II, SPEA-2, and MO-CMA-ES. Methods to compare algorithms will be detailed including test functions and measures to analyze the obtained approximated Pareto-optimal set and front. The numerical limitations of the presented algorithms will be clarified. Methods to cope with such limitations will be described and in particular how to handle the course of dimensionality of the Pareto-front. Finally, multi-criteria decision-making approaches will be presented, and their combination with multi-objective optimization will be described. A focus will be given on demonstrations from different application areas such as: mechanical design, robotics and control. Students will be allowed to make a programming project in one of these areas or in other areas such as: machine learning, aeronautical design, environmental engineering, industrial engineering, and electrical engineering.