A Comprehensive Review of Swarm Optimization Algorithms

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...

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Bibliographic Details
Published in:PloS one Vol. 10; no. 5; p. e0122827
Main Authors: Ab Wahab, Mohd Nadhir, Nefti-Meziani, Samia, Atyabi, Adham
Format: Journal Article
Language:English
Published: United States Public Library of Science 18.05.2015
Public Library of Science (PLoS)
Subjects:
Algorithms
Analysis
Animals
Ants - physiology
Applied mathematics
Bees - physiology
Behavior, Animal
Biogeography
Biological Evolution
Birds - physiology
Chromosomes
Comparative analysis
Computer engineering
Computer science
Computer Simulation
Elitism
Evolutionary algorithms
Genetic algorithms
International conferences
Models, Biological
Mutation
Optimization
Optimization algorithms
Optimization theory
Particle swarm optimization
Performance evaluation
Population
Robotics
Selection, Genetic
Social Behavior
Statistical analysis
Statistical tests
Studies
Swarm intelligence
Systems Biology
Australia
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary: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.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: MNAW AA. Performed the experiments: MNAW AA. Analyzed the data: AA. Contributed reagents/materials/analysis tools: MNAW. Wrote the paper: MNAW SNM AA.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0122827