An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints
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This paper is concerned with the definition of new derivative-free methods for box constrained multiobjective optimization. The method that we propose is a non-trivial extension of the well-known implicit filtering algorithm to the multiobjective case. Global convergence results are stated under smooth assumptions on the objective functions. We also show how the proposed method can be used as a tool to enhance the performance of the Direct MultiSearch (DMS) algorithm. Numerical results on a set of test problems show the efficiency of the implicit filtering algorithm when used to find a single Pareto solution of the problem. Furthermore, we also show through numerical experience that the proposed algorithm improves the performance of DMS alone when used to reconstruct the entire Pareto front.
KeywordsMultiobjective nonlinear programming Derivative-free optimization Implicit filtering
Mathematics Subject Classification90C30 90C56 65K05
We are thankful to three anonymous reviewers whose stimulating comments and suggestions greatly helped us improving the paper. Also, we would like to thank Prof. Ana Luísa Custódio, José F. Aguilar Madeira, A. Ismael F. Vaz, and Luís Nunes Vicente for providing us the matlab code of their direct multisearch algorithm (DMS). Work partially supported by INDAM-GNCS.
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