An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-objective Evolutionary Algorithms

Alexandru-Ciprian Zavoianu, E. Lughofer, G. Bramerdorfer, W. Amrhein, E. Klement
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引用次数: 15

Abstract

The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high quality surrogate models in a relatively short time interval.
基于集成的多目标进化算法动态代理适应度函数生成方法
设计电力驱动系统的任务是一个多目标优化问题(MOOP),即使使用粒子群优化和进化算法等最先进的方法,该问题仍然非常缓慢,因为用于评估提议设计质量的适应度函数是基于时间密集的有限元(FE)模拟。一种直接的解决方案是用更快评估的替代函数替换原始的基于fe的适应度函数。在我们的特殊情况下,每个优化场景都提出了相当独特的挑战(即,目标和约束),代理模型需要在进化算法运行期间实时、自动地构建。在本研究中,使用三个工业MOOPs,我们研究了几种创建此类代理模型的方法,并发现使用多层感知器神经网络和pareto修剪训练集的集成策略能够在相对较短的时间间隔内生成非常高质量的代理模型。
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