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Granularity-based surrogate-assisted particle swarm optimization for high-dimensional expensive optimization

机译:基于粒度的代理辅助粒子群算法进行高维昂贵优化

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摘要

Surrogate-assisted meta-heuristic algorithms have won more and more attention for solving computationally expensive problems over past decades. However, most existing surrogate-assisted meta-heuristic algorithms either require thousands of expensive exact function evaluations to obtain acceptable solutions, or focus on solving only low-dimensional expensive optimization problems. In this paper, we attempt to propose a new method to solve high-dimensional expensive optimization problems, in which the population will firstly be granulated into two subsets, i.e., coarse-grained individuals and fine-grained ones, then different approximation methods are proposed for each category, and finally a new infill criteria is adopted to select solutions that have maximum uncertainty among all coarse-grained individuals and that among all fine-grained individuals, and the solution that has minimal approximated fitness value, to be re-evaluated using the exact objective function. Experimental results comparing the proposed algorithm with a few state-of-the-art surrogate-assisted evolutionary algorithms on benchmark problems with 50 and 100 dimensions show that the proposed algorithm is able to achieve better results when solving high-dimensional multi-modal expensive problems with a limited budget on exact fitness evaluations. (C) 2019 Elsevier B.V. All rights reserved.
机译:在过去的几十年中,代理辅助的元启发式算法赢得了越来越多的关注,用于解决计算量大的问题。但是,大多数现有的代理辅助元启发式算法要么需要数千个昂贵的精确函数评估才能获得可接受的解决方案,要么仅专注于解决低维的昂贵优化问题。本文尝试提出一种新的方法来解决高维代价高昂的优化问题,即首先将总体分为两个子集,即粗粒度个体和细粒度个体,然后提出不同的近似方法对于每个类别,最后采用新的填充标准来选择在所有粗粒度个体和所有细粒度个体中具有最大不确定性的解决方案,以及具有最小近似适应性值的解决方案,以使用确切的目标函数。实验结果将提出的算法与一些最新的替代辅助进化算法对50维和100维基准问题进行了比较,结果表明,该算法在解决高维多模态昂贵问题时能够取得更好的结果预算有限,无法进行确切的体能评估。 (C)2019 Elsevier B.V.保留所有权利。

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