Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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Optimization of an engineering system or component makes a series of changes in the initial random solution(s) iteratively to form the final optimal shape. When multiple conflicting objectives are considered, recent studies on innovization revealed the fact that the set of Pareto-optimal solutions portray certain common design principles. In this paper, we consider a 14-variable bi-objective design optimization of a MEMS device and identify a number of such common design principles through a recently proposed automated innovization procedure. Although these design principles are found to exist among near-Pareto-optimal solutions, the main crux of this paper lies in a demonstration of temporal evolution of these principles during the course of optimization. The results reveal that certain important design principles start to evolve early on, whereas some detailed design principles get constructed later during optimization. Interestingly, there exists a simile between evolution of design principles with that of human evolution. Such information about the hierarchy of key design principles should enable designers to have a deeper understanding of their problems.