Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems
Runge Kutta (RUN) is an up-to-date and well-founded metaheuristic algorithm. The RUN algorithm aims to find the global best in solving problems by going beyond the traps of metaphors. For this purpose, enhanced solution quality mechanism is used to avoid local optimum solutions and increase the conv...
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Düzce University
2021-12-01
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| Series: | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/2047230 |
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| author | Çağrı Suiçmez Hamdi Kahraman Cemal Yılmaz Enes Cengiz |
| author_facet | Çağrı Suiçmez Hamdi Kahraman Cemal Yılmaz Enes Cengiz |
| author_sort | Çağrı Suiçmez |
| collection | DOAJ |
| description | Runge Kutta (RUN) is an up-to-date and well-founded metaheuristic algorithm. The RUN algorithm aims to find the global best in solving problems by going beyond the traps of metaphors. For this purpose, enhanced solution quality mechanism is used to avoid local optimum solutions and increase the convergence speed. Although the RUN algorithm offers promising solutions, it is seen that this algorithm has shortcomings, especially in solving high dimensional multimodal problems. In this study, the solution candidates that guide the search process in the RUN algorithm are developed using the Fitness-Distance Balance (FDB) method. Thus, using the FDB-based RUN algorithm, the global optimum value of many optimization problems will be obtained in the future. CEC 2020 which has current benchmark problems was used to test the performance of the developed FDB-RUN algorithm. 10 different unconstrained benchmark problems taken from CEC 2020 were designed by arranging them in 30/50/100 dimensions. Experimental studies were carried out using the designed benchmark problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it was seen that the FDB-RUN variations showed a superior performance compared to the base algorithm (RUN) in all experimental studies. In particular, it has been shown to provide more effective results for the continuous optimization of high-dimensional problems. |
| format | Article |
| id | doaj-art-76fe2abed46b4ae1aa72dfe556a4c751 |
| institution | Kabale University |
| issn | 2148-2446 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Düzce University |
| record_format | Article |
| series | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| spelling | doaj-art-76fe2abed46b4ae1aa72dfe556a4c7512025-08-20T03:54:07ZengDüzce UniversityDüzce Üniversitesi Bilim ve Teknoloji Dergisi2148-24462021-12-019613514910.29130/dubited.101494797Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional ProblemsÇağrı Suiçmez0https://orcid.org/0000-0002-9709-2276Hamdi Kahraman1https://orcid.org/0000-0001-9985-6324Cemal Yılmaz2https://orcid.org/0000-0003-2053-052XEnes Cengiz3https://orcid.org/0000-0003-1127-2194GAZI UNIVERSITYKARADENIZ TECHNICAL UNIVERSITYMingachevir State UniversityAFYON KOCATEPE ÜNİVERSİTESİRunge Kutta (RUN) is an up-to-date and well-founded metaheuristic algorithm. The RUN algorithm aims to find the global best in solving problems by going beyond the traps of metaphors. For this purpose, enhanced solution quality mechanism is used to avoid local optimum solutions and increase the convergence speed. Although the RUN algorithm offers promising solutions, it is seen that this algorithm has shortcomings, especially in solving high dimensional multimodal problems. In this study, the solution candidates that guide the search process in the RUN algorithm are developed using the Fitness-Distance Balance (FDB) method. Thus, using the FDB-based RUN algorithm, the global optimum value of many optimization problems will be obtained in the future. CEC 2020 which has current benchmark problems was used to test the performance of the developed FDB-RUN algorithm. 10 different unconstrained benchmark problems taken from CEC 2020 were designed by arranging them in 30/50/100 dimensions. Experimental studies were carried out using the designed benchmark problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it was seen that the FDB-RUN variations showed a superior performance compared to the base algorithm (RUN) in all experimental studies. In particular, it has been shown to provide more effective results for the continuous optimization of high-dimensional problems.https://dergipark.org.tr/tr/download/article-file/2047230meta-heuristic searchrunge kutta algorithmfitness-distance balance (fdb)benchmark problemsmeta-sezgisel aramarunge kutta algoritmasıuygunluk-mesafe dengesi (fdb)kıyaslama problemleri |
| spellingShingle | Çağrı Suiçmez Hamdi Kahraman Cemal Yılmaz Enes Cengiz Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems Düzce Üniversitesi Bilim ve Teknoloji Dergisi meta-heuristic search runge kutta algorithm fitness-distance balance (fdb) benchmark problems meta-sezgisel arama runge kutta algoritması uygunluk-mesafe dengesi (fdb) kıyaslama problemleri |
| title | Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems |
| title_full | Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems |
| title_fullStr | Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems |
| title_full_unstemmed | Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems |
| title_short | Improved Runge Kutta Optimizer with Fitness Distance Balance-Based Guiding Mechanism for Global Optimization of High-Dimensional Problems |
| title_sort | improved runge kutta optimizer with fitness distance balance based guiding mechanism for global optimization of high dimensional problems |
| topic | meta-heuristic search runge kutta algorithm fitness-distance balance (fdb) benchmark problems meta-sezgisel arama runge kutta algoritması uygunluk-mesafe dengesi (fdb) kıyaslama problemleri |
| url | https://dergipark.org.tr/tr/download/article-file/2047230 |
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