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<p>This research introduces a novel metaheuristic approach called the Discrete Integer Metaheuristic Optimization Algorithm to enhance the efficiency of truss optimization processes involving discrete size variables and integer values. The proposed algorithm employs two distinct search scenarios: randomized exploration within plus-minus 10% of the leader point, and point-centric search with dynamic perturbation controlled by a Diversity Enhancement Parameter. The effectiveness of the proposed algorithm was evaluated on three benchmark truss optimization problems: a planar truss with 45 members, a space truss consisting of 198 elements, and a 160-bar space truss. The obtained results demonstrate that the proposed algorithm demonstrates improved performance, achieving average weight reductions of 15.3%, 21.2%, and 8.4% compared to the Marine Predators Algorithm, Whale Optimization Algorithm, and Enhanced Vibrating Particles System respectively for the three test cases. The algorithm also shows faster convergence, requiring 32% fewer iterations on average to reach optimal solutions. These results highlight the proposed algorithm’s potential for advancing truss optimization techniques in engineering applications involving discrete and integer variables, leading to more efficient and economical structural designs.</p>

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Enhancing optimization efficiency through discrete integer metaheuristic optimization algorithm (DIMOA): a novel metaheuristic approach for truss optimization

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • M. Paknahad,
  • P. Hosseini,
  • S. J. S. Hakim

摘要

This research introduces a novel metaheuristic approach called the Discrete Integer Metaheuristic Optimization Algorithm to enhance the efficiency of truss optimization processes involving discrete size variables and integer values. The proposed algorithm employs two distinct search scenarios: randomized exploration within plus-minus 10% of the leader point, and point-centric search with dynamic perturbation controlled by a Diversity Enhancement Parameter. The effectiveness of the proposed algorithm was evaluated on three benchmark truss optimization problems: a planar truss with 45 members, a space truss consisting of 198 elements, and a 160-bar space truss. The obtained results demonstrate that the proposed algorithm demonstrates improved performance, achieving average weight reductions of 15.3%, 21.2%, and 8.4% compared to the Marine Predators Algorithm, Whale Optimization Algorithm, and Enhanced Vibrating Particles System respectively for the three test cases. The algorithm also shows faster convergence, requiring 32% fewer iterations on average to reach optimal solutions. These results highlight the proposed algorithm’s potential for advancing truss optimization techniques in engineering applications involving discrete and integer variables, leading to more efficient and economical structural designs.