A new human-based metaheuristic algorithm for solving optimization problems based on preschool education
Abstract In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspirati...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48462-1 |
| _version_ | 1797398026952638464 |
|---|---|
| author | Pavel Trojovský |
| author_facet | Pavel Trojovský |
| author_sort | Pavel Trojovský |
| collection | DOAJ |
| description | Abstract In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications. |
| first_indexed | 2024-03-09T01:18:48Z |
| format | Article |
| id | doaj.art-9eb6657daf644a8d9c813e4e3d6662d2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T01:18:48Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9eb6657daf644a8d9c813e4e3d6662d22023-12-10T12:18:07ZengNature PortfolioScientific Reports2045-23222023-12-0113113110.1038/s41598-023-48462-1A new human-based metaheuristic algorithm for solving optimization problems based on preschool educationPavel Trojovský0Department of Mathematics, Faculty of Science, University of Hradec KrálovéAbstract In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration–exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications.https://doi.org/10.1038/s41598-023-48462-1 |
| spellingShingle | Pavel Trojovský A new human-based metaheuristic algorithm for solving optimization problems based on preschool education Scientific Reports |
| title | A new human-based metaheuristic algorithm for solving optimization problems based on preschool education |
| title_full | A new human-based metaheuristic algorithm for solving optimization problems based on preschool education |
| title_fullStr | A new human-based metaheuristic algorithm for solving optimization problems based on preschool education |
| title_full_unstemmed | A new human-based metaheuristic algorithm for solving optimization problems based on preschool education |
| title_short | A new human-based metaheuristic algorithm for solving optimization problems based on preschool education |
| title_sort | new human based metaheuristic algorithm for solving optimization problems based on preschool education |
| url | https://doi.org/10.1038/s41598-023-48462-1 |
| work_keys_str_mv | AT paveltrojovsky anewhumanbasedmetaheuristicalgorithmforsolvingoptimizationproblemsbasedonpreschooleducation AT paveltrojovsky newhumanbasedmetaheuristicalgorithmforsolvingoptimizationproblemsbasedonpreschooleducation |