Abstract
An evolution strategy, approximating a single Pareto-optimal solution in an objective space exhibiting more than two objectives, is proposed as a cost-effective method (individual-based approach). Moreover, a standard NSGA-II code is modified in order to control the spread-out of unfeasible solutions in a three-dimensional objective space (population-based approach). Both methods are validated by means of a case study, i.e. the optimal design of a magnetic micro-switch.
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