Differential Evolution Assisted by Surrogate Models for Structural Optimization Problems
نویسنده
چکیده
Differential evolution (DE) is a popular computational method used to solve optimization problems with several variants available in the literature. Here, the use of a similarity-based surrogate model is proposed in order to improve DE’s overall performance in computationally expensive problems. The offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess the performance of the proposed procedure. The surrogate assisted DE techniques presented here are compared to standard versions of DE using different variants. The experiments are composed by six different optimization problems involving five structures with continuous as well as discrete design variables.
منابع مشابه
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus...
متن کاملGP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models
This paper proposes a novel surrogate-model-based multiobjective evolutionary algorithm called Differential Evolution for Multiobjective Optimization Based on Gaussian Process Models (GP-DEMO). The algorithm is based on the newly defined relations for comparing solutions under uncertainty. These relations minimize the possibility of wrongly performed comparisons of solutions due to inaccurate s...
متن کاملA multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems
Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...
متن کاملOPTIMAL CONSTRAINED DESIGN OF STEEL STRUCTURES BY DIFFERENTIAL EVOLUTIONARY ALGORITHMS
Structural optimization, when approached by conventional (gradient based) minimization algorithms presents several difficulties, mainly related to computational aspects for the huge number of nonlinear analyses required, that regard both Objective Functions (OFs) and Constraints. Moreover, from the early '80s to today's, Evolutionary Algorithms have been successfully developed and applied as a ...
متن کاملSurrogate-assisted evolutionary computation: Recent advances and future challenges
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012