چکیده (انگلیسی):

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization
method based on the social behaviors of birds flocking or fish schooling. Although PSO is represented in
solving many well-known numerical test problems, but it suffers from the premature convergence. A
number of basic variations have been developed due to solve the premature convergence problem and
improve quality of solution founded by the PSO. This study presents a comprehensive survey of the
various PSO-based algorithms. As part of this survey, we include a classification of the approaches and
we identify the main features of each proposal. In the last part of the study, some of the topics within this
field that are considered as promising areas of future research are listed.