Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems
Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search...
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| Published in | Multimedia tools and applications Vol. 83; no. 11; pp. 32613 - 32653 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2024
Springer Nature B.V Springer Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1573-7721 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-023-16890-w |
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| Abstract | Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods. |
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| AbstractList | Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods. |
| Author | Hussien, Abdelazim G Ezugwu, Absalom E. Abuhaija, Belal Shinwan, Mohammad Al Abualigah, Laith Khodadadi, Nima Zitar, Raed Abu Gul, Faiza Oliva, Diego Jia, Heming |
| Author_xml | – sequence: 1 givenname: Laith orcidid: 0000-0002-2203-4549 surname: Abualigah fullname: Abualigah, Laith email: aligah.2020@gmail.com organization: Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Department of Electrical and Computer Engineering, Lebanese American University, MEU Research Unit, Middle East University, Applied Science Research Center, Applied Science Private University, School of Computer Sciences, Universiti Sains Malaysia, School of Engineering and Technology, Sunway University Malaysia – sequence: 2 givenname: Diego surname: Oliva fullname: Oliva, Diego organization: Depto. de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI – sequence: 3 givenname: Heming surname: Jia fullname: Jia, Heming organization: School of Information Engineering, Sanming University – sequence: 4 givenname: Faiza surname: Gul fullname: Gul, Faiza organization: Department of Electrical Engineering, Aerospace and Avionics, Campus Kamra, Air University – sequence: 5 givenname: Nima surname: Khodadadi fullname: Khodadadi, Nima organization: Department of Civil and Environmental Engineering, Florida International University – sequence: 6 givenname: Abdelazim G surname: Hussien fullname: Hussien, Abdelazim G organization: Department of Computer and Information Science, Linköping University, Faculty of Science, Fayoum University – sequence: 7 givenname: Mohammad Al surname: Shinwan fullname: Shinwan, Mohammad Al organization: Faculty of Information Technology, Applied Science Private University – sequence: 8 givenname: Absalom E. surname: Ezugwu fullname: Ezugwu, Absalom E. organization: Unit for Data Science and Computing, North-West University – sequence: 9 givenname: Belal surname: Abuhaija fullname: Abuhaija, Belal email: babuhaij@kean.edu organization: Department of Computer Science, Wenzhou-Kean University – sequence: 10 givenname: Raed Abu surname: Zitar fullname: Zitar, Raed Abu organization: Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi |
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| Keywords | Benchmark functions Prairie dog optimization algorithm Optimization problems Dwarf mongoose optimization algorithm Meta-heuristics |
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| SubjectTerms | Algorithms Benchmarks Computer Communication Networks Computer Science Convergence Data Structures and Information Theory Evolution Mathematical functions Methods Multimedia Multimedia Information Systems Optimization Optimization algorithms Optimization techniques Parameter identification Physics Prairie dogs Search methods Search process Snakes Special Purpose and Application-Based Systems |
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| Title | Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems |
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