This study presents the vectorization of metaheuristic algorithms as the first stage of vectorized optimization implementation. Vectorization is a technique for converting an algorithm, which operates on a single value at a time to one that operates on a collection of values at a time to execute rapidly. The vectorization technique also operates by replacing multiple iterations into a single operation, which improves the algorithm's performance in speed and makes the algorithm simpler and easier to be implemented. It is important to optimize the algorithm by implementing the vectorization technique, which improves the program's performance, which requires less time and can run long-running test functions faster, also execute test functions that cannot be implemented in non-vectorized algorithms and reduces iterations and time complexity. Converting to vectorization to operate several values at once and enhance algorithms' speed and efficiency is a solution for long running times and complicated algorithms. The objective of this study is to use the vectorization technique on one of the metaheuristic algorithms and compare the results of the vectorized algorithm with the algorithm which is non-vectorized.

翻译：本研究提出将元启发式算法的向量化作为向量化优化实现的第一阶段。向量化是一种将每次操作单个值的算法转换为每次操作一组值以加快执行速度的技术。该技术还通过将多次迭代替换为单次操作来提升算法在速度方面的性能，同时使算法更简洁且易于实现。采用向量化技术对算法进行优化至关重要——这能提升程序性能、缩短运行时间、更快执行长期运行的测试函数，并能运行无法在非向量化算法中实现的测试函数，同时减少迭代次数并降低时间复杂度。将算法转换为向量化形式以实现同时操作多个值、提升算法的速度与效率，是解决运行耗时过长及算法复杂问题的有效方案。本研究旨在对某一种元启发式算法应用向量化技术，并将向量化后的算法与非向量化算法进行结果对比分析。