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Stochastic Method for the Solution of Unconstrained Vector Optimization Problems

Author

Listed:
  • S. Schäffler

    (Universität der Bundeswehr, EIT 1)

  • R. Schultz

    (Siemens AG)

  • K. Weinzierl

    (Siemens AG)

Abstract

We propose a new stochastic algorithm for the solution of unconstrained vector optimization problems, which is based on a special class of stochastic differential equations. An efficient algorithm for the numerical solution of the stochastic differential equation is developed. Interesting properties of the algorithm enable the treatment of problems with a large number of variables. Numerical results are given.

Suggested Citation

  • S. Schäffler & R. Schultz & K. Weinzierl, 2002. "Stochastic Method for the Solution of Unconstrained Vector Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 114(1), pages 209-222, July.
    • Handle: RePEc:spr:joptap:v:114:y:2002:i:1:d:10.1023_a:1015472306888
    DOI: 10.1023/A:1015472306888
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    References listed on IDEAS

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    1. Ralph L. Keeney, 1972. "Utility Functions for Multiattributed Consequences," Management Science, INFORMS, vol. 18(5-Part-1), pages 276-287, January.
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    Cited by:

    1. Oliver Schütze & Adanay Martín & Adriana Lara & Sergio Alvarado & Eduardo Salinas & Carlos Coello, 2016. "The directed search method for multi-objective memetic algorithms," Computational Optimization and Applications, Springer, vol. 63(2), pages 305-332, March.
    2. Bennet Gebken & Sebastian Peitz, 2021. "An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 188(3), pages 696-723, March.
    3. Stefan Banholzer & Giulia Fabrini & Lars Grüne & Stefan Volkwein, 2020. "Multiobjective Model Predictive Control of a Parabolic Advection-Diffusion-Reaction Equation," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    4. Shukla, Pradyumn Kumar & Deb, Kalyanmoy, 2007. "On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1630-1652, September.
    5. Oliver Schütze & Víctor Adrián Sosa Hernández & Heike Trautmann & Günter Rudolph, 2016. "The hypervolume based directed search method for multi-objective optimization problems," Journal of Heuristics, Springer, vol. 22(3), pages 273-300, June.
    6. O. Schütze & C. Hernández & E-G. Talbi & J. Q. Sun & Y. Naranjani & F.-R. Xiong, 2019. "Archivers for the representation of the set of approximate solutions for MOPs," Journal of Heuristics, Springer, vol. 25(1), pages 71-105, February.
    7. Carlos Ignacio Hernández Castellanos & Oliver Schütze & Jian-Qiao Sun & Guillermo Morales-Luna & Sina Ober-Blöbaum, 2020. "Numerical Computation of Lightly Multi-Objective Robust Optimal Solutions by Means of Generalized Cell Mapping," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    8. Clempner, Julio B. & Poznyak, Alexander S., 2016. "Solving the Pareto front for multiobjective Markov chains using the minimum Euclidean distance gradient-based optimization method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 142-160.
    9. Fereshteh Akbari & Mehrdad Ghaznavi & Esmaile Khorram, 2018. "A Revised Pascoletti–Serafini Scalarization Method for Multiobjective Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 178(2), pages 560-590, August.

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