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<p>The Hannibal Barca Optimizer (HBO) introduces a novel heuristic optimization paradigm inspired by the ancient carthaginian general’s strategic ingenuity. HBO adeptly tackles complex, multi-dimensional optimization challenges with innovation by incorporating the strategic principles of Hannibal’s renowned pincer movement and integrating them with a new advanced optimization methodology called parallax learning. This algorithm leverages a tripartite strategy, akin to the stages of the battle of Cannae, utilizing a population-based approach to simulate the tactical envelopment and decisiveness of Hannibal’s forces in computational environments. The proposed HBO algorithm was evaluated against eight recent and well-established metaheuristics using the CEC2022 test suite to verify its reliability. Additionally, it was tested on thirteen classical engineering design problems. To further demonstrate its efficacy, the HBO was applied to an image multi-thresholding problem. Experimental results indicate that the HBO algorithm is more competitive than existing state-of-the-art meta-heuristic methods when assessed using the CEC’22 benchmarks. Furthermore, it outperforms seven out of thirteen real-world optimization problems observed in the study. Additionally, HBO proves to be the most competitive in terms of the multi-thresholding problem.</p>

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Hannibal Barca optimizer: the power of the pincer movement for global optimization and multilevel image thresholding

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Mohamed Wajdi Ouertani,
  • Ghaith Manita,
  • Ouajdi Korbaa

摘要

The Hannibal Barca Optimizer (HBO) introduces a novel heuristic optimization paradigm inspired by the ancient carthaginian general’s strategic ingenuity. HBO adeptly tackles complex, multi-dimensional optimization challenges with innovation by incorporating the strategic principles of Hannibal’s renowned pincer movement and integrating them with a new advanced optimization methodology called parallax learning. This algorithm leverages a tripartite strategy, akin to the stages of the battle of Cannae, utilizing a population-based approach to simulate the tactical envelopment and decisiveness of Hannibal’s forces in computational environments. The proposed HBO algorithm was evaluated against eight recent and well-established metaheuristics using the CEC2022 test suite to verify its reliability. Additionally, it was tested on thirteen classical engineering design problems. To further demonstrate its efficacy, the HBO was applied to an image multi-thresholding problem. Experimental results indicate that the HBO algorithm is more competitive than existing state-of-the-art meta-heuristic methods when assessed using the CEC’22 benchmarks. Furthermore, it outperforms seven out of thirteen real-world optimization problems observed in the study. Additionally, HBO proves to be the most competitive in terms of the multi-thresholding problem.