Adaptive Particle Swarm Optimization

By Zhi-hui Zhan, Jun Zhang. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2008

2,028 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/1830ea5680a5b71f2f45d252601b4e1e84c66b3d

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Citations in the corpus

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, background): “performance to resource scheduling [Zhan et al. 2009; Di et al. 2014].”
Improved artificial bee colony algorithm for global optimization (2011, methodology): “[25] SaDE Self-adaptive differential evolution algorithm Qin and Suganthan [26] jDE Self-adapting control parameters in differential evolution Brest, et al.”
Genetic Learning Particle Swarm Optimization (2016, background): “These variants can be generally divided into four categories that focus on: 1) population topology and multiswarm techniques [8]–[10]; 2) parameter control [11]–[14]; 3) hybrid methods [15]–[25]; and 4) novel learning schemes [26]–[30], respectively.”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “[54] used statistical method to analyze population distribution information and fitness information of PSO.”
Parameter tuning for configuring and analyzing evolutionary algorithms (2011, methodology): “It has been successfully applied in various domains of metaheuristics, including Evolution Strategies [10], Genetic Algorithms [11], Differential Evolution [12,13] and Particle Swarm Optimization [14].”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “Another important idea is to integrate PSO with local search techniques [30], [31].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “Functions CPSO-H [1] CLPSO [34] APSO [59] GOPSO [53] DNSCLPSO DNSPSO”
A review of population-based meta-heuristic algorithm (2013, methodology): “Although PSO is easy to implement, it may face up to the slow convergence rate, parameter selection problem and easily get trapped in a local optimum due to its poor exploration when solving complex multimodal problems [54, 55, 56].”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “Apart from adapting x; c1 and c2, additional parameters to help regulate these three parameters have also been introduced [95,33].”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, methodology): “A standard PSO is an evolutionary meta ‐ heuristics algorithm adopted in many fields [32 ‐ 37].”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “In order to enhance the performance of PSO, a number of PSO variants have been proposed, including the parameter adaptation based variants [44,20], the new topological structure based variants [26,9], and the hybridization based variants [7,37], to name a few.”
A social spider algorithm for global optimization (2015, methodology): “Another test which can examine the reliability of stochastic algorithms is the success rate test and this test has been adopted in many previous work, e.g. [32][43].”
Self regulating particle swarm optimization algorithm (2015, background): “Recently, an adaptive parameter selection scheme for both the inertia weight and acceleration coefficients have been proposed in [79, 2].”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, background): “variable parameters [27], [28], changing the update equation [29], [30], adopting the operators of other EAs [31], [32], and so on.”