VAPI: Vectorization of Algorithm for Performance Improvement

TL;DR

This paper introduces a vectorization approach for metaheuristic algorithms to significantly enhance their speed, efficiency, and simplicity by replacing iterative processes with bulk operations, enabling faster execution of complex and long-running functions.

Contribution

It is the first to apply vectorization to metaheuristic algorithms, demonstrating performance improvements and simplifying implementation compared to traditional methods.

Findings

Vectorized algorithms run faster than non-vectorized counterparts.

Performance improvements include reduced execution time and complexity.

Vectorization enables handling of functions that are infeasible with non-vectorized algorithms.

Abstract

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.