IMPROVING METHOD FOR ACHIEVING ENGINEERING DESIGN OPTIMIZATION BASED ON MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM

摘要

PPROBLEM TO BE SOLVED: To provide a system and a method for obtaining a set of better converged and diversified Pareto optimal solutions in an engineering design optimization of a product (such as an automobile or a cellular phone). PSOLUTION: In one aspect, a plurality of MOEA based engineering optimizations of a product are conducted independently. Each of the plurality of engineering design optimizations differs from the other engineering design optimizations with parameters such as initial generation and/or evolutionary algorithm. For example, populations (design options) of initial generation can be created randomly from different seed of a random or pseudo-random number generator. In another aspect, each optimization employs a particular revolutionary algorithm including, but not limited to, NSGA-II (nondominated sorting genetic algorithm-II), SPEA (strength Pareto evolutionary algorithm), etc. Furthermore, each independently conducted optimization's Pareto optimal solutions are combined to create a set of better converged and diversified solutions. Combinations can be performed at one or more predefined checkpoints during the evolution process of the optimization. PCOPYRIGHT: (C)2011,JPO&INPIT