Abstract
As computation tools have evolved, along with the emergence of industrial breakthroughs, there has been a proliferation of engineering design real world problems, in the form of multiobjective problems models. Solving this type of problems using multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years.
In this paper, we will focus on the most up-to-date and efficient evolutionary multiobjective algorithms (EMO). The majority of these algorithms have been tested on theoretical test problems, in order to validate the obtained results in terms of convergence and diversity. In this work, the test will be built out of engineering design real world problems, to verify the extent to which MOEAs are capable of producing good results.
We will proceed as follows: Present the (MOEAs) used and adjust the parameters of the algorithms in order to obtain the best results, choose different problems in terms of objective functions and constraints, model problems with Matlab and solve them using the Platemo platform, to eventually comment and compare the different obtained results.
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Amamou, Y., Jebari, K. (2023). Multiobjective Evolutionary Algorithms for Engineering Design Problems. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-031-26384-2_28
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DOI: https://doi.org/10.1007/978-3-031-26384-2_28
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