What is it about?
Particle swarm optimization (PSO) is a heuristic global optimization method motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation.
Featured Image
Photo by Marius Niveri on Unsplash
Why is it important?
PSO gathered considerable interest from the natural computing research community and has been seen to offer rapid and effective optimization for complex multidimensional search spaces, with adaptations to multiple objectives and constrained optimization. PSO has been acknowledged widely as a probable global optimizing algorithm because of its convenience of realization, and low constraints on the environment and objective functions
Perspectives
I would recommend this survey to researchers in the field most especially those who are new in the subject area because it proposes a new method of controlling the PSO algorithm with the implementation of Sparse Representation which focuses on the convergence speed, solution diversity, and well-distributed pareto front set. The paper also opens up more discussions on the implementation PSO algorithm
Dr. Ben-Bright Benuwa
Data Link Institute
Read the Original
This page is a summary of: A Comprehensive Review of Particle Swarm Optimization, International Journal of Engineering Research in Africa, April 2016, Trans Tech Publications,
DOI: 10.4028/www.scientific.net/jera.23.141.
You can read the full text:
Contributors
The following have contributed to this page
