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Erschienen in:
Differential Opposition-Based Particle Swarm Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Divisive Multi-level Differential Evolution

verfasst von : Huifang Zhang, Wei Huang, Jinsong Wang

Erschienen in: Computational Intelligence and Intelligent Systems

Verlag: Springer Singapore

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Abstract

It is generally accepted that the clustering-based differential evolution (CDE) algorithm exhibits better performance in comparison with the standard differential evolution. However, such clustering method mechanism that is only based on input data may lead to some limitations such as premature convergence. In this study, we propose a divisive multi-level differential evolution algorithm (DMDE) to alleviate this drawback. The proposed divisive method is based not only input data but also the output fitness. In particular, DMDE becomes the conventional CDE when the output fitness in not considered in the process of clustering. Several benchmark functions are included to evaluate the performance of the proposed DMDE. Experimental results show that the proposed DMDE exhibits a promising performance when compared with CDE, especially in case of high-dimensional continuous optimization problems.

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Vorheriges Kapitel Optimization of UWB Antenna Based on Particle Swarm Optimization Algorithm
Nächstes Kapitel A Comparative Summary of the Latest Version of MapReduce Parallel and Old Version from the Perspective of Framework
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Metadaten
Titel
A Divisive Multi-level Differential Evolution
verfasst von
Huifang Zhang
Wei Huang
Jinsong Wang
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-13-1650-0

Electronic ISBN: 978-981-13-1651-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-1651-7_8