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The obstacles in design engineering arise from the boundary conditions and objectives to be met within the constraints. These challenges range from machinery, energy, automotive, industrial, and even construction sectors. For these kinds of problems, metaheuristic optimization algorithms that search large search spaces at once while evading local optima are being adopted more often. This study approaches three engineering design problems—the tubular column, corrugated bulkhead, and cantilever beam—using three novel metaheuristic frameworks termed as Puma Optimizer (PO), Greylag Goose Optimization (GGO), and Human Evolutionary Optimization Algorithm (HEOA). Each of the algorithms was tried out on each of the problems for 30 trials per problem, with the maximum reliability achieved with proper documentation done during the trials. The results of the study proved that PO was superior to GGO and HEOA in every scenario tested. This type of comparative study offers quantitative data for researchers developing the subject as well as a strong starting point for the choice of appropriate methods for solving actual engineering problems.

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Investigating the Performance of Recently Proposed Metaheuristic Optimization Algorithms on Real-World Engineering Design Problems

  • 会议论文

  • 发表日期：

    2026年

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  • Alper Buğra Polat,
  • Elif Varol-Altay,
  • Osman Altay

摘要

The obstacles in design engineering arise from the boundary conditions and objectives to be met within the constraints. These challenges range from machinery, energy, automotive, industrial, and even construction sectors. For these kinds of problems, metaheuristic optimization algorithms that search large search spaces at once while evading local optima are being adopted more often. This study approaches three engineering design problems—the tubular column, corrugated bulkhead, and cantilever beam—using three novel metaheuristic frameworks termed as Puma Optimizer (PO), Greylag Goose Optimization (GGO), and Human Evolutionary Optimization Algorithm (HEOA). Each of the algorithms was tried out on each of the problems for 30 trials per problem, with the maximum reliability achieved with proper documentation done during the trials. The results of the study proved that PO was superior to GGO and HEOA in every scenario tested. This type of comparative study offers quantitative data for researchers developing the subject as well as a strong starting point for the choice of appropriate methods for solving actual engineering problems.