Automated Solution Selection in Multi-objective Optimisation
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This paper proposes an approach to the solution of multi-objective optimisation problems that delivers a single, preferred solution. A conventional, population-based, multiobjective optimisation method is used to provide a set of solutions approximating the Pareto front. As the set of solutions evolves, an approximation to the Pareto front is derived using a Kriging method. This approximate surface is traversed using a single objective optimisation method, driven by a simple, aggregated objective function that expresses design preferences. The approach is demonstrated using a combination of multi-objective particle swarm optimisation (MOPSO) and the Simplex method of Nelder and Mead, applied to several, standard, multi-objective test problems. Good, compromise solutions meeting user-defined design preferences are delivered without manual intervention.
IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence).
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