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Erschienen in:
Insulin Chart Prediction for Diabetic Patients Using Hidden Markov Model (HMM) and Simulated Annealing Method Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

A Diversity-Based Comparative Study for Advance Variants of Differential Evolution

verfasst von : Prashant Singh Rana, Kavita Sharma, Mahua Bhattacharya, Anupam Shukla, Harish Sharma

Erschienen in: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Verlag: Springer India

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Abstract

Differential evolution (DE) is a vector population-based stochastic search optimization algorithm. DE converges faster, finds the global optimum independent to initial parameters, and uses few control parameters. The exploration and exploitation are the two important diversity characteristics of population-based stochastic search optimization algorithms. Exploration and exploitation are compliment to each other, i.e., a better exploration results in worse exploitation and vice versa. The objective of an efficient algorithm is to maintain the proper balance between exploration and exploitation. This paper focuses on a comparative study based on diversity measures for DE and its prominent variants, namely JADE, jDE, OBDE, and SaDE.

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Vorheriges Kapitel Palmprint Recognition Using Geometrical and Statistical Constraints
Nächstes Kapitel Computing Vectors Based Document Clustering and Numerical Result Analysis
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Metadaten
Titel
A Diversity-Based Comparative Study for Advance Variants of Differential Evolution
verfasst von
Prashant Singh Rana
Kavita Sharma
Mahua Bhattacharya
Anupam Shukla
Harish Sharma
Copyright-Jahr
2014
Verlag
Springer India
Buch
Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Print ISBN: 978-81-322-1601-8

Electronic ISBN: 978-81-322-1602-5

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-81-322-1602-5_137