title: A comprehensive review of swarm optimization algorithms.
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.
authors.affiliation: Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom.
authors.forename: Mohd Nadhir
authors.initials: MN
authors.lastname: Ab Wahab
authors.affiliation: Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom.
authors.forename: Samia
authors.initials: S
authors.lastname: Nefti-Meziani
authors.affiliation: 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.
authors.forename: Adham
authors.initials: A
authors.lastname: Atyabi
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology; D001516:Bees / Q000502:physiology; D001522:Behavior, Animal; D005075:Biological Evolution; D001717:Birds / Q000502:physiology; D003198:Computer Simulation; D008954:Models, Biological; D012641:Selection, Genetic; D012919:Social Behavior; D049490:Systems Biology