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Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting

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Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting. / Epitropakis, Michael; Tasoulis, Dimitrios K; Pavlidis, Nicos et al.
2012 IEEE Congress on Evolutionary Computation (CEC2012) . IEEE, 2012.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Epitropakis, M, Tasoulis, DK, Pavlidis, N, Plagianakos, VP & Vrahatis, MN 2012, Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting. in 2012 IEEE Congress on Evolutionary Computation (CEC2012) . IEEE. https://doi.org/10.1109/CEC.2012.6256425

APA

Epitropakis, M., Tasoulis, D. K., Pavlidis, N., Plagianakos, V. P., & Vrahatis, M. N. (2012). Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting. In 2012 IEEE Congress on Evolutionary Computation (CEC2012) IEEE. https://doi.org/10.1109/CEC.2012.6256425

Vancouver

Epitropakis M, Tasoulis DK, Pavlidis N, Plagianakos VP, Vrahatis MN. Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting. In 2012 IEEE Congress on Evolutionary Computation (CEC2012) . IEEE. 2012 doi: 10.1109/CEC.2012.6256425

Author

Epitropakis, Michael ; Tasoulis, Dimitrios K ; Pavlidis, Nicos et al. / Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting. 2012 IEEE Congress on Evolutionary Computation (CEC2012) . IEEE, 2012.

Bibtex

@inproceedings{f8d33ff6cdce4640b9dba3bc8ff502b9,
title = "Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting",
abstract = "An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand",
author = "Michael Epitropakis and Tasoulis, {Dimitrios K} and Nicos Pavlidis and Plagianakos, {Vassilis P.} and Vrahatis, {Michael N.}",
year = "2012",
month = jun,
day = "10",
doi = "10.1109/CEC.2012.6256425",
language = "English",
isbn = "978-1-4673-1510-4",
booktitle = "2012 IEEE Congress on Evolutionary Computation (CEC2012)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting

AU - Epitropakis, Michael

AU - Tasoulis, Dimitrios K

AU - Pavlidis, Nicos

AU - Plagianakos, Vassilis P.

AU - Vrahatis, Michael N.

PY - 2012/6/10

Y1 - 2012/6/10

N2 - An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand

AB - An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand

U2 - 10.1109/CEC.2012.6256425

DO - 10.1109/CEC.2012.6256425

M3 - Conference contribution/Paper

SN - 978-1-4673-1510-4

BT - 2012 IEEE Congress on Evolutionary Computation (CEC2012)

PB - IEEE

ER -