多目标优化
先验与后验
数学优化
计算机科学
进化算法
帕累托原理
进化规划
帧(网络)
进化计算
工程设计过程
偏爱
最优化问题
人工智能
机器学习
数学
工程类
机械工程
电信
哲学
统计
认识论
作者
Gilberto Reynoso-Meza,Javier Sanchis,Xavier Blasco,Sergio García-Nieto
标识
DOI:10.1016/j.asoc.2014.07.009
摘要
Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.
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