Automated Solution Selection in Multi-objective Optimisation
MetadataShow full item record
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).
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.