This study presents a population-based evolutionary optimization algorithm (Adaptive Differential Evolution with Diversification Strategies or ADEDS). The algorithm developed using the sinusoidal objective function and subsequently evaluated with a wide-ranging set of 22 benchmark functions, including Rosenbrock, Rastrigin, Ackley, and DeVilliersGlasser02, among others. The study employs single-objective optimization in a two-dimensional space and runs ADEDS on each of the benchmark functions with multiple iterations. In terms of convergence speed and solution quality, ADEDS consistently outperforms standard DE for a variety of optimization challenges, including functions with numerous local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy functions. This effectiveness holds great promise for optimizing supply chain operations, driving cost reductions, and ultimately enhancing overall performance. The findings imply the importance of effective optimization strategy for improving supply chain efficiency, reducing costs, and enhancing overall performance.

翻译：本研究提出了一种基于种群的进化优化算法（带多样化策略的自适应差分进化算法，简称ADEDS）。该算法使用正弦目标函数开发，并通过涵盖Rosenbrock、Rastrigin、Ackley、DeVilliersGlasser02等在内的22个广泛基准函数进行评估。研究采用二维空间下的单目标优化方法，对每个基准函数进行多次迭代运行ADEDS算法。在收敛速度与解质量方面，ADEDS在多种优化挑战中始终优于标准差分进化算法，这些挑战包括含大量局部最优、平板形、谷形、拉伸形及含噪函数。该有效性对优化供应链运营、推动成本降低并最终提升整体性能具有重要潜力。研究结果表明，有效的优化策略对于提升供应链效率、降低成本及增强整体性能具有关键意义。