Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures
The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is cru...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8741862 |
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| author | Ching-Yun Kao Shih-Lin Hung Budy Setiawan |
| author_facet | Ching-Yun Kao Shih-Lin Hung Budy Setiawan |
| author_sort | Ching-Yun Kao |
| collection | DOAJ |
| description | The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely, integrated mutation and adaptive mutation factor strategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1, DE/best/2, and DE/rand-to-best/1) are combined in the integrated mutation strategy to increase the diversity of random search and avoid premature convergence to a local minimum. The adaptive mutation factor strategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures. |
| format | Article |
| id | doaj-art-4f79de58fa7f4b469c60b23cc797193e |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4f79de58fa7f4b469c60b23cc797193e2025-02-03T06:06:32ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/87418628741862Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss StructuresChing-Yun Kao0Shih-Lin Hung1Budy Setiawan2Department of Applied Geoinformatics, Chia Nan University of Pharmacy & Science, No. 60, Sec. 1, Erren Rd., Tainan 71710, TaiwanDepartment of Civil Engineering, National Chiao Tung University, No. 1001, University Rd., Hsinchu 300, TaiwanDepartment of Civil Engineering, National Chiao Tung University, No. 1001, University Rd., Hsinchu 300, TaiwanThe performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely, integrated mutation and adaptive mutation factor strategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1, DE/best/2, and DE/rand-to-best/1) are combined in the integrated mutation strategy to increase the diversity of random search and avoid premature convergence to a local minimum. The adaptive mutation factor strategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures.http://dx.doi.org/10.1155/2020/8741862 |
| spellingShingle | Ching-Yun Kao Shih-Lin Hung Budy Setiawan Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures Advances in Civil Engineering |
| title | Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures |
| title_full | Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures |
| title_fullStr | Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures |
| title_full_unstemmed | Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures |
| title_short | Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures |
| title_sort | two strategies to improve the differential evolution algorithm for optimizing design of truss structures |
| url | http://dx.doi.org/10.1155/2020/8741862 |
| work_keys_str_mv | AT chingyunkao twostrategiestoimprovethedifferentialevolutionalgorithmforoptimizingdesignoftrussstructures AT shihlinhung twostrategiestoimprovethedifferentialevolutionalgorithmforoptimizingdesignoftrussstructures AT budysetiawan twostrategiestoimprovethedifferentialevolutionalgorithmforoptimizingdesignoftrussstructures |