Volume 16, Issue 1 (1-2026)
IJOCE 2026, 16(1): 31-47 |
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Shahrouzi M, Naserifar Y, Taghavi A M, Emamzadeh S. A HYBRID DIFFERENTIAL EVOLUTION AND SWARM ALGORITHM FOR ENGINEERING OPTIMIZATION. IJOCE 2026; 16 (1) :31-47
URL: http://ijoce.iust.ac.ir/article-1-661-en.html
URL: http://ijoce.iust.ac.ir/article-1-661-en.html
1- Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
Abstract: (86 Views)
Although metaheuristic algorithms are popular tools for global optimization, none of them is reported as the best for all problems. Hybridization is an advanced solution to overcome the shortcomings of individual methods by using the power points of the others. Here, a popular swarm intelligent algorithm with high explorative capability is combined with an exploitative operator of differential evolution and some dynamic parameter variation, as well as a greedy operator to enhance the search refinement. The proposed method is evaluated on a variety of engineering and constrained engineering problems, including the optimal design of Belleville Spring, pressure vessel, car side impact problem, and Morrow point dam. According to the results, considerable improvement is observed with respect to the standard particle swarm optimizer as well as competitive performance with a number of metaheuristic algorithms.
Keywords: Hybrid metaheuristic algorithms, differential evolution, particle swarm optimizer, mechanical engineering benchmarks, concrete dam, shape optimization
Type of Study: Research |
Subject:
Optimal design
Received: 2025/11/20 | Accepted: 2026/01/8 | Published: 2026/01/17
Received: 2025/11/20 | Accepted: 2026/01/8 | Published: 2026/01/17
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