@Article{10798587.2017.1293881,
AUTHOR = {Dongping Tian},
TITLE = {Particle Swarm Optimization with Chaos-based Initialization for Numerical  Optimization},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {331--342},
URL = {http://www.techscience.com/iasc/v24n2/39759},
ISSN = {2326-005X},
ABSTRACT = {Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been 
deeply studied and widely applied to a variety of problems. However, it is easily trapped into the 
local optima and premature convergence appears when solving complex multimodal problems. To 
address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent 
and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the 
standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed 
particles to improve the quality of the initial population. On the other hand, Gaussian mutation as 
well as the local re-initialization strategy based on the maximal focus distance is exploited to help 
the algorithm escape from the local optima and make the particles proceed with searching in other 
regions of the solution space. In addition, an auxiliary velocity-position update strategy is exclusively 
used for the global best particle, which can effectively guarantee the convergence of the proposed 
particle swarm optimization. Extensive experiments on eight well-known benchmark functions with 
different dimensions demonstrate that the proposed PSO is superior or highly competitive to several 
state-of-the-art PSO variants in dealing with complex multimodal problems.},
DOI = {10.1080/10798587.2017.1293881}
}