Interactive multi-objective optimization for simulated moving bed processes
Jussi Hakanen, Yoshiaki Kawajiri, Kaisa Miettinen, Lorenz Biegler (2007)
Control and Cybernetics
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Displaying similar documents to “A method of approximating Pareto sets for assessments of implicit Pareto set elements”
Interactive multi-objective optimization for simulated moving bed processes
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An approach to finding trade-off solutions by a linear transformation of objective functions
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LFS functions in multi-objective programming
Luka Neralić, Sanjo Zlobec (1996)
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We find conditions, in multi-objective convex programming with nonsmooth functions, when the sets of efficient (Pareto) and properly efficient solutions coincide. This occurs, in particular, when all functions have locally flat surfaces (LFS). In the absence of the LFS property the two sets are generally different and the characterizations of efficient solutions assume an asymptotic form for problems with three or more variables. The results are applied to a problem in highway construction,...
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Optimization shape of variable capacitance micromotor using differential evolution algorithm.
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High-performance simulation-based algorithms for an alpine ski racer’s trajectory optimization in heterogeneous computer systems
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International Journal of Applied Mathematics and Computer Science
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Two characterizations of Pareto minima in convex multicriteria optimization
Sanjo Zlobec (1984)
Aplikace matematiky
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Two conditions are given each of which is both necessary and sufficient for a point to be a global Pareto minimum. The first one is obtained by studying programs where each criterion appears as a single objective function, while the second one is given in terms of a "restricted Lagrangian". The conditions are compared with the familiar characterizations of properly efficient and weakly efficient points of Karlin and Geoffrion.
Multi-objective Optimization Problem with Bounded Parameters
Ajay Kumar Bhurjee, Geetanjali Panda (2014)
RAIRO - Operations Research - Recherche Opérationnelle
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In this paper, we propose a nonlinear multi-objective optimization problem whose parameters in the objective functions and constraints vary in between some lower and upper bounds. Existence of the efficient solution of this model is studied and gradient based as well as gradient free optimality conditions are derived. The theoretical developments are illustrated through numerical examples.