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<p>The Quantum Whale Optimization Algorithm (QWOA), a novel nature-inspired, quantum-based metaheuristic optimization algorithm, is proposed in this study. This algorithm inherits the social behaviour of humpback whales, specifically their unique hunting approach, from WOA and adopts/integrates the concept of local attractor in quantum mechanics. QWOA is compared with nine algorithms including existing WOA variants. A nonlinear production-inventory model is formulated, incorporating dynamic power-based demand and nonlinear holding costs. The demand for products is also influenced by nonlinear factors such as selling price, green level, and warranty period. Additionally, the model includes carbon emission reduction investment as part of its formulation. To evaluate the robustness of the proposed algorithm, twenty-three classical mathematical benchmarks and IEEE CEC 2022 benchmarks are solved and compared with QWOA. The optimization results demonstrate that QWOA is highly competitive when compared to state-of-the-art metaheuristic algorithms. For critical comparison over the chosen metaheuristics, we used both parametric (ANOVA) and non-parametric (Kruskal–Wallis) tests. First order sensitivity analysis is performed for the inventory problem in order to make fruitful conclusion over the inventory model.</p>

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A novel Quantum Whale Optimization Algorithm for sustainable production inventory under nonlinear demand and holding costs

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Pritam Kumar Pakhira,
  • Hachen Ali,
  • Fleming Akhtar,
  • Ali Akbar Shaikh,
  • Seyedali Mirjalili

摘要

The Quantum Whale Optimization Algorithm (QWOA), a novel nature-inspired, quantum-based metaheuristic optimization algorithm, is proposed in this study. This algorithm inherits the social behaviour of humpback whales, specifically their unique hunting approach, from WOA and adopts/integrates the concept of local attractor in quantum mechanics. QWOA is compared with nine algorithms including existing WOA variants. A nonlinear production-inventory model is formulated, incorporating dynamic power-based demand and nonlinear holding costs. The demand for products is also influenced by nonlinear factors such as selling price, green level, and warranty period. Additionally, the model includes carbon emission reduction investment as part of its formulation. To evaluate the robustness of the proposed algorithm, twenty-three classical mathematical benchmarks and IEEE CEC 2022 benchmarks are solved and compared with QWOA. The optimization results demonstrate that QWOA is highly competitive when compared to state-of-the-art metaheuristic algorithms. For critical comparison over the chosen metaheuristics, we used both parametric (ANOVA) and non-parametric (Kruskal–Wallis) tests. First order sensitivity analysis is performed for the inventory problem in order to make fruitful conclusion over the inventory model.