Improved particle swarm optimization combined with chaos

By Bo Liu, Ling Wang, Yihui Jin, Fang Tang, Dexian Huang. 2005

969 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c417a90c244dcdd622fa3bdec5bd9fb3dd360f7f

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Citations in the corpus

CHAOTIC PARTICLE SWARM OPTIMIZATION FOR ECONOMIC DISPATCH CONSIDERING THE GENERATOR CONSTRAINTS (2007, methodology): “A particle status on the search space is characterized by two factors: its position and velocity, which are updated by the following equations [15,16,25]:”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “Other specific search operators have also been incorporated into PSO, including mutation operators [29,51,61], local search operators [83,86], and some nature-inspired operators as well, such as the niching PSO [5], culture-based PSO [13] and the aging theory inspired PSO (ALC-PSO) [8].”
Reservoir flood control operation based on chaotic particle swarm optimization algorithm (2014, methodology): “Owing to the difference of computing results between two successive iterations is always great using simple COA, it is usually combined with other intelligent optimization technique, such as swarm evolutionary algorithm [19], genetic algorithm (GA) [20–22], particle swarm optimization (PSO) algorith”
Rational approximation for fractional-order system by particle swarm optimization (2012, methodology): “In [24, 25], the chaotic optimization was applied in PSO to avoid the prema-”
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process (2007, background): “The position of the ith particle is presented by a vector Xi = (xi1; . . . ; xid; . . . ; xiD), where xid 2 [xmin;d; xmax;d]; d = 1; . . . ; D. xmin;d and xmax;d are the lower and upper bounds for the dth dimension, respectively.”
Particle swarm and ant colony algorithms hybridized for improved continuous optimization (2007, methodology): “[9] also used this function to investigate the curse of dimensionality on the CPSO algorithm, which was run 10 times for Ackley function with different dimensions.”
On the efficiency of chaos optimization algorithms for global optimization (2007, methodology): “Since we have cut the ‘bad’ part of chaotic variable interval (0,1) during the generation of chaotic sequences, which is distinct from the conventional chaos optimization algorithms in the papers [15–25], the presented hybrid optimization algorithm enhances the computational efficiency significantly”
Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “In chaotic PSO [54], an adaptive inertia weight factor is used to balance exploration and exploitation.”
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (2007, background): “Some studies have been carried out to prevent premature convergence and to balance the exploration and exploitation abilities [ 35 ].”
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification (2012, methodology): “We perform the Holm procedure (Demšar, 2006; García t al., 2010; Luengo et al., 2009) to evaluate performance of proosed model with other classifier.”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “Other hybrid PSO variants include PSO with Gaussian mutation [34], PSO with chaos [35], orthogonal learning PSO (OLPSO) [36], PSO with a moderate-random-search strategy [37], and a very recently proposed PSO with a periodic mutation strategy and neural networks [38].”