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Memetic Computing
Erschienen in: Memetic Computing 2/2018

07.07.2016 | Regular Research Paper

A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems

verfasst von: Chaoli Sun, Jinliang Ding, Jianchao Zeng, Yaochu Jin

Erschienen in: Memetic Computing | Ausgabe 2/2018

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Abstract

Surrogate assisted meta-heuristic algorithms have received increasing attention over the past years due to the fact that many real-world optimization problems are computationally expensive. However, most existing surrogate assisted meta-heuristic algorithms are designed for small or medium scale problems. In this paper, a fitness approximation assisted competitive swarm optimizer is proposed for optimization of large scale expensive problems. Different from most surrogate assisted evolutionary algorithms that use a computational model for approximating the fitness, we estimate the fitness based on the positional relationship between individuals in the competitive swarm optimizer. Empirical study on seven widely used benchmark problems with 100 and 500 decision variables show that the proposed fitness approximation assisted competitive swarm optimizer is able to achieve competitive performance on a limited computational budget.
Vorheriger Artikel Editorial
Nächster Artikel Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation

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Metadaten
Titel
A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems
verfasst von
Chaoli Sun
Jinliang Ding
Jianchao Zeng
Yaochu Jin
Publikationsdatum
07.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 2/2018
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-016-0199-9

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