<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures.</p>
<p>The cross-entropy method is used as a stochastic model-based method to solve the multiobjective optimization problem and reach a first elite set of global solutions. In the local search step, an ∈-constraint method converts the multiobjective optimization problem to a series of parameterized single-objective optimization problems. Then, sequential quadratic programming (SQP) is used to solve the derived single-objective optimization problems allowing to reinforce and improve the global results. Numerical examples are used to demonstrate the efficiency and effectiveness of the proposed approach.</p>
</abstract>

This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures.
The cross-entropy method is used as a stochastic model-based method to solve the multiobjective optimization problem and reach a first elite set of global solutions. In the local search step, an ∈-constraint method converts the multiobjective optimization problem to a series of parameterized single-objective optimization problems. Then, sequential quadratic programming (SQP) is used to solve the derived single-objective optimization problems allowing to reinforce and improve the global results. Numerical examples are used to demonstrate the efficiency and effectiveness of the proposed approach.

Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

Laboratory of Analysis Mathematics and Applications, Department of Mathematics, Faculty of Sciences

Moroccan Journal of Pure and Applied Analysis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local...

Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

Data publikacji: 29 sty 2021

Zakres stron: 299 - 311

Otrzymano: 31 sie 2020

Przyjęty: 02 sty 2021

DOI: https://doi.org/10.2478/mjpaa-2021-0019

Słowa kluczowe
Multiobjective optimization, Cross-entropy method, ∈-constraint method, SQP methods

© 2021 Abdelmajid Ezzine et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

Data publikacji: 29 sty 2021

Zakres stron: 299 - 311

Otrzymano: 31 sie 2020

Przyjęty: 02 sty 2021

DOI: https://doi.org/10.2478/mjpaa-2021-0019

Słowa kluczoweMultiobjective optimization, Cross-entropy method, ∈-constraint method, SQP methods

© 2021 Abdelmajid Ezzine et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Multiobjective optimization, Cross-entropy method, ∈-constraint method, SQP methods