Optimisation: Improving problem formulation and human interaction
By Neil Cantle and Corey Grigg
30 June 2017
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.
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