Abstract
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.
| Original language | English |
|---|---|
| Article number | 432 |
| Journal | Journal of Supercomputing |
| Volume | 81 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Jan 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 12 Responsible Consumption and Production
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SDG 13 Climate Action
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SDG 17 Partnerships for the Goals
Keywords
- CEC2018 benchmark
- Electromagnetic field optimization
- Engineering design optimization
- Global optimization
- Hybrid optimization algorithms
- Multi-tracker optimization
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