Preference Focussed Many-Objective Evolutionary Computation

This source preferred by Shahin Rostami

Authors: Rostami, S.

Editors: Bowring, N.

Solving complex real-world problems often involves the simultaneous optimisation of multiple conflicting performance criteria, these real-world problems occur in the fields of engineering, economics, chemistry, manufacturing, physics and many more. The optimisation process usually involves some design challenges in the form of the optimisation of a number of objectives and constraints. There exist many traditional optimisation methods (calculus based, random search, enumerative, etc...), however, these only offer a single solution in either adequate performance in a narrow problem domain or inadequate performance across a broad problem domain.

Evolutionary Multi-objective Optimisation (EMO) algorithms are robust optimisers which are suitable for solving complex real-world multi-objective optimisation problems, as they are able to address each of the conflicting objectives simultaneously. Typically, these EMO algorithms are run non-interactively with a Decision Maker (DM) setting the initial parameters of the algorithm and then analysing the results at the end of the optimisation process. When EMO is applied to real-world optimisation problems there is often a DM who is only interested in a portion of the Pareto-optimal front, however, incorporation of DM preferences is often neglected in the EMO literature.

In this thesis, the incorporation of DM preferences into EMO search methods has been explored. This has been achieved through the review of EMO literature to identify a powerful method of variation, Covariance Matrix Adaptation (CMA), and its computationally infeasible EMO implementation, MO-CMA-ES. A CMA driven EMO algorithm, CMA-PAES, capable of optimisation in the presence of many objectives has been developed, benchmarked, and statistically verified to outperform MO-CMA-ES and MOEA/D-DRA on selected test suites. CMA-PAES and MOEA/D-DRA with the incorporation of the novel Weighted Z-score (WZ) preference articulation operator (supporting a priori, a posteriori or progressive incorporation) are then benchmarked on a range of synthetic and real-world problems. WZ-CMA-PAES is then successfully applied to a real-world problem regarding the optimisation of a classifier for concealed weapon detection, outperforming previously published classifier implementations.

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