Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021491" target="_blank" >RIV/62690094:18470/24:50021491 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10586-023-04221-5" target="_blank" >https://link.springer.com/article/10.1007/s10586-023-04221-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10586-023-04221-5" target="_blank" >10.1007/s10586-023-04221-5</a>

Result language

angličtina

Original language name

Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

Original language description

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm&apos;s performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/157231-puma-optimizer-po.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Cluster Computing

ISSN

1386-7857

e-ISSN

1573-7543

Volume of the periodical

27

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

49

Pages from-to

5235-5283

UT code for WoS article

001145076400005

EID of the result in the Scopus database

2-s2.0-85182682775