LEVYEFO-WTMTOA: the hybrid of the multi-tracker optimization algorithm and the electromagnetic field optimization

被引:0
|
作者
Safi-Esfahani, Faramarz [1 ,4 ]
Mohammadhoseini, Leili [1 ,2 ]
Larian, Habib [1 ,2 ]
Mirjalili, Seyedali [3 ]
机构
[1] Islamic Azad Univ, Fac Comp Engn, Najafabad Branch, Najafabad, Iran
[2] Islamic Azad Univ, Big Data Res Ctr, Najafabad Branch, Najafabad, Iran
[3] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimisat, Brisbane, Qld 4006, Australia
[4] Univ Technol Sydney, Fac Engn & IT, Sch Informat Syst & Modelling, Sydney, NSW 2007, Australia
来源
JOURNAL OF SUPERCOMPUTING | 2025年 / 81卷 / 02期
关键词
Hybrid optimization algorithms; Electromagnetic field optimization; Multi-tracker optimization; Engineering design optimization; CEC2018; benchmark; Global optimization; PARTICLE SWARM OPTIMIZATION; CHICKEN SWARM; EVOLUTION;
D O I
10.1007/s11227-024-06856-6
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The field of engineering optimization often faces significant challenges in efficiently exploring complex solution spaces to identify optimal configurations, frequently struggling with local optima and premature convergence. These issues are especially pronounced in traditional optimization algorithms when applied to high-dimensional or intricate engineering problems. This paper introduces the LEVYEFO-WTMTOA algorithm, an innovative hybrid that combines the modified multi-tracker optimization algorithm (MTOA) with the electromagnetic field optimization (EFO) approach. This integration effectively addresses the limitations of previous methods, such as stagnation in local optima and suboptimal search strategies. The evaluations using the CEC2018 benchmark suite demonstrate that the LEVYEFO-WTMTOA algorithm significantly outperforms existing algorithms, reducing the mean error by an average of 20%. Specifically, the presented algorithm achieved a maximum cost improvement of 31.03% in spring design and 32.15% in welded beam design. These results confirm the LEVYEFO-WTMTOA's superior capability in handling complex optimization tasks, offering a powerful tool for algorithmic design in engineering applications and setting a new benchmark for performance in the field.
引用
收藏
页数:54
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