Confidence-based robust optimisation using multi-objective meta-heuristics

Mirjalili, Seyedali; Lewis, Andrew; Dong, Jin Song

doi:10.1016/j.swevo.2018.04.002

Cited by 19 publications

(4 citation statements)

References 66 publications

(90 reference statements)

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“…Due to the difficulties of handling uncertainties, there are some robust design optimization test functions published in the literature [38,[176][177][178][179][180]. Reference [38] proved the ineffectiveness of the normal PSO and genetic algorithms on these test problems.…”

Section: Measures For Evolutionary Algorithms and Uncertaintiesmentioning

confidence: 99%

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

et al. 2020

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The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.

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Section: Measures For Evolutionary Algorithms and Uncertaintiesmentioning

confidence: 99%

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

et al. 2020

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“…Note that using this function it is possible to create test problems where the robustness of the solutions can be tested in all objectives. Recent work on robust optimization uses test functions where robustness can be controlled for only one objective or uses common test problems [47,44,45,48].…”

Section: Auxiliary Function G(x)mentioning

confidence: 99%

Scalable and customizable benchmark problems for many-objective optimization

Meneghini

Alves

Gaspar‐Cunha

et al. 2020

Applied Soft Computing

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Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.

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“…Robustness has been taken into consideration in two different ways. Firstly, with an expectation measure, secondly, as a constraint of the optimization task [22]. Both methodologies increase the computational complexity of the design problem.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tolerance Analysis of Permanent Magnet Machines with Encapsuled FEM Models Using Digital-Twin-Distiller

Orosz¹,

Gadó²,

Katona

et al. 2021

Processes

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Tolerance analysis is crucial in every manufacturing process, such as electrical machine design, because tight tolerances lead to high manufacturing costs. A FEM-based solution of the tolerance analysis of an electrical machine can easily lead to a computationally expensive problem. Many papers have proposed the design of experiments, surrogate-model-based methodologies, to reduce the computational demand of this problem. However, these papers did not focus on the information loss and the limitations of the applied methodologies. Regardless, the absolute value of the calculated tolerance and the numerical error of the applied numerical methods can be in the same order of magnitude. In this paper, the tolerance and the sensitivity of BLDC machines’ cogging torque are analysed using different methodologies. The results show that the manufacturing tolerances can have a significant effect on the calculated parameters, and that the mean value of the calculated cogging torque increases. The design of the experiment-based methodologies significantly reduced the calculation time, and shows that the encapsulated FEM model can be invoked from an external system-level optimization to examine the design from different aspects.

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Confidence-based robust optimisation using multi-objective meta-heuristics

Cited by 19 publications

References 66 publications

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

Robust Design Optimization and Emerging Technologies for Electrical Machines: Challenges and Open Problems

Scalable and customizable benchmark problems for many-objective optimization

Automatic Tolerance Analysis of Permanent Magnet Machines with Encapsuled FEM Models Using Digital-Twin-Distiller

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