Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization
Harmony search algorithm (HSA) is extensively utilized in engineering optimization. Nevertheless, it encounters problems of slow convergence and reduced accuracy, which hinder its capability to escape local optima. This paper proposes HSA-DELF, a novel hybrid algorithm that combines differential evo...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10840216/ |
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| author | Feng Qin Azlan Mohd Zain Kai-Qing Zhou Norfadzlan Bin Yusup Didik Dwi Prasetya Rozita Abdul Jalil Zaheera Zainal Abidin Mahadi Bahari Yusri Kamin Mazlina Abdul Majid |
| author_facet | Feng Qin Azlan Mohd Zain Kai-Qing Zhou Norfadzlan Bin Yusup Didik Dwi Prasetya Rozita Abdul Jalil Zaheera Zainal Abidin Mahadi Bahari Yusri Kamin Mazlina Abdul Majid |
| author_sort | Feng Qin |
| collection | DOAJ |
| description | Harmony search algorithm (HSA) is extensively utilized in engineering optimization. Nevertheless, it encounters problems of slow convergence and reduced accuracy, which hinder its capability to escape local optima. This paper proposes HSA-DELF, a novel hybrid algorithm that combines differential evolution (DE) and Lévy flight (LF) techniques to enhance the performance of HSA. HSA-DELF leverages multi-mutation strategies of DE and LF random walk combined with weighted individuals to improve exploration and exploitation based on population fitness standard deviation comparison, and adopts pairwise iterative updates of the population to achieve faster convergence and higher solution quality. Extensive experiments were conducted to validate performance on 23 classic benchmark functions and 12 CEC 2022 benchmark functions, followed by comprehensive testing on 7 engineering problems, demonstrating the superiority of HSA-DELF. Comparative analysis with 5 well-known algorithms (HSA, DE, CSA, GA, and PSO) and 4 HSA variants (IHS, MHSA, IHSDE, and IMGHSA) confirmed the robustness of HSA-DELF. Statistical results, including best, mean, standard deviation, and worst values, consistently highlight the superior performance of HSA-DELF in terms of convergence speed, solution quality, and robustness. The Wilcoxon signed-rank test further corroborated these significant advantages. HSA-DELF showed notable improvements in 6 out of 7 engineering problems, achieving an accuracy of 85.71%. This study establishes HSA-DELF as an effective and reliable method for solving complex engineering optimization problems. |
| format | Article |
| id | doaj-art-52a5688376514592ba053c6cd5398f51 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-52a5688376514592ba053c6cd5398f512025-01-28T00:01:05ZengIEEEIEEE Access2169-35362025-01-0113135341357210.1109/ACCESS.2025.352971410840216Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering OptimizationFeng Qin0https://orcid.org/0000-0003-2369-145XAzlan Mohd Zain1https://orcid.org/0000-0003-2004-3289Kai-Qing Zhou2Norfadzlan Bin Yusup3https://orcid.org/0000-0002-8913-8203Didik Dwi Prasetya4https://orcid.org/0000-0002-3540-2961Rozita Abdul Jalil5https://orcid.org/0009-0003-2597-4039Zaheera Zainal Abidin6https://orcid.org/0000-0003-4868-941XMahadi Bahari7https://orcid.org/0000-0003-0301-374XYusri Kamin8Mazlina Abdul Majid9https://orcid.org/0000-0001-9068-7368Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaSchool of Communication and Electronic Engineering, Jishou University, Jishou, ChinaFaculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, MalaysiaFaculty of Engineering, State University of Malang, Malang, IndonesiaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, MalaysiaFaculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, MalaysiaFaculty of Management, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Gambang, Pahang, MalaysiaHarmony search algorithm (HSA) is extensively utilized in engineering optimization. Nevertheless, it encounters problems of slow convergence and reduced accuracy, which hinder its capability to escape local optima. This paper proposes HSA-DELF, a novel hybrid algorithm that combines differential evolution (DE) and Lévy flight (LF) techniques to enhance the performance of HSA. HSA-DELF leverages multi-mutation strategies of DE and LF random walk combined with weighted individuals to improve exploration and exploitation based on population fitness standard deviation comparison, and adopts pairwise iterative updates of the population to achieve faster convergence and higher solution quality. Extensive experiments were conducted to validate performance on 23 classic benchmark functions and 12 CEC 2022 benchmark functions, followed by comprehensive testing on 7 engineering problems, demonstrating the superiority of HSA-DELF. Comparative analysis with 5 well-known algorithms (HSA, DE, CSA, GA, and PSO) and 4 HSA variants (IHS, MHSA, IHSDE, and IMGHSA) confirmed the robustness of HSA-DELF. Statistical results, including best, mean, standard deviation, and worst values, consistently highlight the superior performance of HSA-DELF in terms of convergence speed, solution quality, and robustness. The Wilcoxon signed-rank test further corroborated these significant advantages. HSA-DELF showed notable improvements in 6 out of 7 engineering problems, achieving an accuracy of 85.71%. This study establishes HSA-DELF as an effective and reliable method for solving complex engineering optimization problems.https://ieeexplore.ieee.org/document/10840216/Harmony search algorithmbenchmark functionsengineering optimizationmulti-mutation strategiespairwise iterative updates |
| spellingShingle | Feng Qin Azlan Mohd Zain Kai-Qing Zhou Norfadzlan Bin Yusup Didik Dwi Prasetya Rozita Abdul Jalil Zaheera Zainal Abidin Mahadi Bahari Yusri Kamin Mazlina Abdul Majid Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization IEEE Access Harmony search algorithm benchmark functions engineering optimization multi-mutation strategies pairwise iterative updates |
| title | Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization |
| title_full | Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization |
| title_fullStr | Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization |
| title_full_unstemmed | Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization |
| title_short | Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization |
| title_sort | hybrid harmony search algorithm integrating differential evolution and l x00e9 vy flight for engineering optimization |
| topic | Harmony search algorithm benchmark functions engineering optimization multi-mutation strategies pairwise iterative updates |
| url | https://ieeexplore.ieee.org/document/10840216/ |
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