Optimisation problems have traditionally been formulated as single objective and solved with the use of gradient-based or direct search methods. Most practical real-world problems involve multiple, often conflicting objectives, and also highly complex search spaces. Competing goals and objectives necessarily give rise to a set of compromise options and solutions. To counteract some of these difficulties, multiple-criteria decision-making is brought together with evolutionary multi-objective optimisation.