When dealing with multiple design variables, it can be difficult finding optimum combinations that are suitable to design objectives. Instead of individual optimal solutions, Pareto curves can be drawn with design proposals evaluated if there is a trade-off in evaluation items.
By using the optimization function in JMAG, Pareto solutions can be derived for the challenges involved with multi-objective optimization.
This document describes the procedure for running multi-objective optimization calculations with dimensions as design variables and correlative evaluation items.
Dimensions, Multi-objective, Optimization, Objective functions, Design Variables, Measurement variable settings