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Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization
Discrete structural optimization is generally regarded as a complicated optimization problem due to the presence of discrete variables. However, since metaheuristic algorithms do not require any gradient information of the objective function and constraints, they are usually well suited for discrete optimization problems. The success-history based adaptive differential evolution (SHADE) algorithm, which is a modified version of the differential evolution (DE) algorithm, employs a historical memory of successful control parameter settings to guide the generation of future control parameter values. Over the last few years, SHADE has been successfully used to solve a number of real-world optimization problems. However, to the best of our knowledge, it has not yet been applied for discrete structural optimization. In this paper, the SHADE algorithm is applied to solve discrete truss optimization problems with stress and displacement constraints. Four benchmark truss optimization problems taken from the literature are used to demonstrate the performance of the SHADE algorithm and the obtained results are presented and compared with those of other state-of-the-art metaheuristic algorithms existing in the literature. Optimization results indicate the excellent performance of the SHADE algorithm compared to other considered algorithms in terms of both solution accuracy and computational cost.
Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization
Discrete structural optimization is generally regarded as a complicated optimization problem due to the presence of discrete variables. However, since metaheuristic algorithms do not require any gradient information of the objective function and constraints, they are usually well suited for discrete optimization problems. The success-history based adaptive differential evolution (SHADE) algorithm, which is a modified version of the differential evolution (DE) algorithm, employs a historical memory of successful control parameter settings to guide the generation of future control parameter values. Over the last few years, SHADE has been successfully used to solve a number of real-world optimization problems. However, to the best of our knowledge, it has not yet been applied for discrete structural optimization. In this paper, the SHADE algorithm is applied to solve discrete truss optimization problems with stress and displacement constraints. Four benchmark truss optimization problems taken from the literature are used to demonstrate the performance of the SHADE algorithm and the obtained results are presented and compared with those of other state-of-the-art metaheuristic algorithms existing in the literature. Optimization results indicate the excellent performance of the SHADE algorithm compared to other considered algorithms in terms of both solution accuracy and computational cost.
Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization
Iran J Sci Technol Trans Civ Eng
Kaveh, Ali (Autor:in) / Biabani Hamedani, Kiarash (Autor:in)
01.02.2025
23 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization
| Springer Verlag | 2025
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