Int. J. Simul. Multidisci. Des. Optim.
Volume 13, 2022
|Number of page(s)||6|
|Published online||06 January 2022|
New multiobjective optimization algorithm using NBI-SASP approaches for mechanical structural problems
LIMSAD, Department of Mathematics and computing, Hassan 2 University, Ain Chock Sciences Faculty, Km8 Route El Jadida, BOP 5366 Maarif, Casablanca, Morocco
2 LM, Department of Physics, Hassan 2 University, Ain Chock Sciences Faculty, Km8 Route El Jadida, BOP 5366 Maarif, Casablanca, Morocco
* e-mail: email@example.com
Accepted: 7 November 2021
Various engineering design problems are formulated as constrained multi-objective optimization problems. One of the relevant and popular methods that deals with these problems is the weighted method. However, the major inconvenience with its application is that it does not yield a well distributed set. In this study, the use of the Normal Boundary Intersection approach (NBI) is proposed, which is effective in obtaining an evenly distributed set of points in the Pareto set. Given an evenly distributed set of weights, it can be strictly shown that this approach is absolutely independent of the relative scales of the functions. Moreover, in order to ensure the convergence to the Global Pareto frontier, NBI approach has to be aligned with a global optimization method. Thus, the following paper suggests NBI-Simulated Annealing Simultaneous Perturbation method (NBI-SASP) as a new method for multiobjective optimization problems. The study shall test also the applicability of the NBI-SASP approach using different engineering multi-objective optimization problems and the findings shall be compared to a method of reference (NSGA). Results clearly demonstrate that the suggested method is more efficient when it comes to search ability and it provides a well distributed global Pareto Front.
Key words: Global optimization / hybrid method / simultaneous perturbation / simulated annealing / multiobjective optimization / normal boundary intersection approach / pareto front
© S. El Moumen and S. Ouhimmou, Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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