A comprehensive review of swarm optimization algorithms

Mohd Nadhir Ab Wahab; Samia Nefti-Meziani; Adham Atyabi

doi:10.1371/journal.pone.0122827

A comprehensive review of swarm optimization algorithms

PLoS One. 2015 May 18;10(5):e0122827. doi: 10.1371/journal.pone.0122827. eCollection 2015.

Authors

Mohd Nadhir Ab Wahab¹, Samia Nefti-Meziani¹, Adham Atyabi²

Affiliations

¹ Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom.
² Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom; School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, Adelaide, Australia.

Abstract

Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms*
Animals
Ants / physiology
Bees / physiology
Behavior, Animal*
Biological Evolution
Birds / physiology
Computer Simulation
Models, Biological*
Selection, Genetic
Social Behavior
Systems Biology

Grants and funding

This research paper was supported by the GAMMA Programme which is funded through the Regional Growth Fund. The Regional Growth Fund (RGF) is a $3.2 billion fund supporting projects and programmes which are using private sector investment to generate economic growth as well as creating sustainable jobs between now and the mid-2020s. For more information, please go to www.bis.gov.uk/rgf. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.