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A mesh adaptive direct search algorithm for multiobjective optimization
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AI
DOI：10.1016/j.ejor.2009.11.010.png)
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Abstract
En 中文
This work studies multiobjective optimization (MOP) of nonsmooth functions subject to general constraints. We first present definitions and optimality conditions as well as some single-objective formulations of MOP, parameterized with respect to some reference point in the space of objective functions. Next, we propose a new algorithm called MULTIMADS (multiobjective mesh adaptive direct search) for MOP. MULTIMADS generates an approximation of the Pareto front by solving a series of single-objective formulations of MOP generated using the NBI (natural boundary intersection) framework. These single-objective problems are solved using the MADS (mesh adaptive direct search) algorithm for constrained nonsmooth optimization. The Pareto front approximation is shown to satisfy some first-order necessary optimality conditions based on the Clarke calculus. MULTIMADS is then tested on problems from the literature with different Pareto front landscapes and on a styrene production process simulation problem from chemical engineering. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Multiobjective optimization
Mesh adaptive direct search (MADS)
Convergence analysis
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W