VecMetaPy: A vectorized framework for metaheuristic optimization in Python

Amir Pouya Hemmasian, Kazem Meidani, Seyedali Mirjalili, Amir Barati Farimani

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This work aims to accelerate metaheuristic optimization algorithms and their experimental environment using the vectorization feature in the NumPy library in Python. It is built upon EvoloPy, a framework previously introduced by Faris et al. (2016)[41] for metaheuristic optimization in Python. We have vectorized every aspect of this framework including function evaluation, random number sampling, strategy selection, and performing the update equations. We compare the wall-clock time of our vectorized framework for a few algorithms with the original implementation in EvoloPy. The results demonstrate the substantial improvement in the algorithm's execution time, even to three orders of magnitude in cases like a 180-dimensional search space. The codes can be found at https://github.com/BaratiLab/VecMetaPy.

Original languageEnglish
Article number103092
JournalAdvances in Engineering Software
Volume166
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Algorithm
  • Metaheuristic
  • Optimization
  • Swarm intelligence
  • Vectorization

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