Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Electromagnetic Optimization by Genetic Algorithms
Electromagnetic Optimization by Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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When tackling the multicriteria optimization of a device in electrical engineering, the exhaustive sampling of Pareto optimal front implies the use of complex and time-consuming algorithms that are unpractical from the industrial viewpoint. In several cases, however, the accurate identification of a few nondominated solutions is often sufficient for the design purposes. An evolutionary methodology of lowest order, dealing with a small number of individuals, is proposed to obtain a cost-effective approximation of non-dominated solutions. In particular, the algorithm assigning the fitness enables the designer to pursue either shape or performance diversity of the device. The optimal shape design of a shielded reactor, based on the optimization of both cost and performance of the device, is presented as a real-life case study.