Evidence map›Paper›PMID 40419534›Full record

ArticleScientific reports2025

An intelligent hybrid grey wolf-particle swarm optimizer for optimization in complex engineering design problem.

Muhammad Suhail Shaikh, Haoyue Lin, Senlin Xie, Xiaoqing Dong, Yifan Lin, Chandan Kumar Shiva, Wulfran Fendzi Mbasso

Abstract read
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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

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5 citing papers in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Muhammad Suhail ShaikhSchool of Physics and Electronic Engineering, Hanshan Normal University, Guangdong, 521000, China.
Haoyue LinSchool of Physics and Electronic Engineering, Hanshan Normal University, Guangdong, 521000, China.
Senlin XieSchool of Physics and Electronic Engineering, Hanshan Normal University, Guangdong, 521000, China.
Xiaoqing DongSchool of Physics and Electronic Engineering, Hanshan Normal University, Guangdong, 521000, China. dxqzq@hstc.edu.cn.
Yifan LinSchool of Physics and Electronic Engineering, Hanshan Normal University, Guangdong, 521000, China. yflin@hstc.edu.cn.
Chandan Kumar ShivaDepartment of Electrical and Electronics Engineering, SR University, Warangal, 506371, India.
Wulfran Fendzi MbassoCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.

Funding

Chaozhou Engineering Technology Research Center,Chaozhou Science and Technology Plan Project (202102GY17, 20237C25, 20237C23).China University Industry, University, and Research Innovation Fund Project (2022XF058),Guangdong Province Higher Education Teaching Reform Project (HSGDJG231370,HSJG-YB231016)Hanshan Normal University project (XY202105)Key Discipline Research Ability Improvement Project of Guangdong Province (2021ZDJS043, 2022ZDJS068),Key Laboratory of Ordinary Universities in Guangdong Province (2022KSYS003),Project of Department of Education of Guangdong Province (2022KTSCX079),Special Projects in Key Fields of Ordinary Universities in Guangdong Province (2022ZDZX3012, 2023ZDZX1013,2023ZDZX2038),This work is supported by the innovation teams of ordinary Universities in Guangdong Province (2023KCXTD022, 2021KCXTD038),
6 · The paper itself

Abstract

Engineering design optimization problems present significant challenges due to the complexity of objective functions, which often involve both continuous and discrete design variables, along with multiple constraints. GWO and PSO are well-known heuristic algorithms with efficient search ability and reasonable execution time. They are often used to solve complex optimization problems, but there are still problems such as premature convergence and limited global search efficiency. To overcome these limitations and enhance solution quality, this study proposes a novel Hybrid Grey Wolf-Particle Swarm Optimization (HGWPSO) algorithm. HGWPSO integrates the exploration ability of GWO with the rapid convergence and exploitation efficiency of PSO. The algorithm's performance is first validated using CEC_2022 benchmark functions and then applied to eight complex engineering design problems, including pressure vessel design, compression spring design, three-bar truss design, gear train design, cantilever beam design, welded beam design, transmission line parameter estimation, and reactive power planning problem. The improvement in the average best optimal value varies across different cases, demonstrating the effectiveness of the proposed method. In Case 1, the improvement is 71.61%, while in Cases 2 and 4 obtained a 99% improvement. Case 3 shows an enhancement of 53.46%, and Case 5 reaches 71.92%, whereas Case 6 also obtained 99%. In Case 7, the improvement depends on the bundle configuration, with two-bundle conductors showing a 76.91% increase, three-bundle conductors achieving 43.94%, and four-bundle conductors reaching 46.57%. Finally, in Case 8, the improvement is 1.02%. The obtained results demonstrate that HGWPSO achieves better performance than other methods in terms of convergence rate, cost function minimization, and constraint handling. This study highlights the effectiveness of HGWPSO as a powerful tool for solving complex engineering design problems.

Indexed as

CEC_2022 functionsEngineering problemGrey wolf optimizerHybridizationParticle swarm optimization

Identifiers

PMID40419534
PMCPMC12106706

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