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。向量化是一种将单值操作算法转换为集合操作算法以提升执行速度的技术。该技术通过将多次迭代合并为单一操作，不仅能提升算法在速度方面的性能，还能使算法更简洁、易于实现。通过实施向量化技术优化算法具有重要意义：既能提升程序性能、减少运行时间、加速长时测试函数的执行，又能执行非向量化算法无法实现的测试函数，同时降低迭代次数与时间复杂度。将算法转换为向量化以同时处理多个数值、增强算法的速度与效率，是应对长运行时间与复杂算法问题的解决方案。本研究旨在对某元启发式算法应用向量化技术，并将向量化算法与非向量化算法的结果进行对比分析。