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

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)...

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Vydáno v:	Cluster computing Ročník 27; číslo 4; s. 5235 - 5283
Hlavní autoři:	Abdollahzadeh, Benyamin, Khodadadi, Nima, Barshandeh, Saeid, Trojovský, Pavel, Gharehchopogh, Farhad Soleimanian, El-kenawy, El-Sayed M., Abualigah, Laith, Mirjalili, Seyedali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.07.2024 Springer Nature B.V
Témata:	Algorithms Animals Benchmarks Clustering Computer Communication Networks Computer Science Data mining Datasets Heuristic Heuristic methods Linear programming Machine learning Mathematical models Methods Operating Systems Optimization Optimization algorithms Optimization techniques Phase change Popularity Processor Architectures Statistical tests Teaching Global optimization Puma optimization algorithm Automatic phase change Metaheuristic algorithm Optimization Machine learning
ISSN:	1386-7857, 1573-7543
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Abstract	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’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 .
AbstractList	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’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. 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’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 .
Author	Gharehchopogh, Farhad Soleimanian Barshandeh, Saeid Trojovský, Pavel El-kenawy, El-Sayed M. Abdollahzadeh, Benyamin Abualigah, Laith Khodadadi, Nima Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Benyamin surname: Abdollahzadeh fullname: Abdollahzadeh, Benyamin organization: Department of Mathematics, Faculty of Science, University of Hradec Králové – sequence: 2 givenname: Nima surname: Khodadadi fullname: Khodadadi, Nima email: nima.khodadadi@miami.edu organization: Department of Civil and Architectural Engineering, University of Miami – sequence: 3 givenname: Saeid surname: Barshandeh fullname: Barshandeh, Saeid organization: Department of Computer Science, School of Engineering, Afagh Higher Education Institute – sequence: 4 givenname: Pavel surname: Trojovský fullname: Trojovský, Pavel organization: Department of Mathematics, Faculty of Science, University of Hradec Králové – sequence: 5 givenname: Farhad Soleimanian surname: Gharehchopogh fullname: Gharehchopogh, Farhad Soleimanian organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 6 givenname: El-Sayed M. surname: El-kenawy fullname: El-kenawy, El-Sayed M. organization: Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology – sequence: 7 givenname: Laith surname: Abualigah fullname: Abualigah, Laith organization: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Computer Sciences Department, University of Tabuk, Computer Science Department, Al al-Bayt University – sequence: 8 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Research and Innovation Center, Obuda University
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Snippet	Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems,...
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SubjectTerms	Algorithms Animals Benchmarks Clustering Computer Communication Networks Computer Science Data mining Datasets Heuristic Heuristic methods Linear programming Machine learning Mathematical models Methods Operating Systems Optimization Optimization algorithms Optimization techniques Phase change Popularity Processor Architectures Statistical tests Teaching
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Title	Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning
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