Recent approaches to global optimization problems through Particle Swarm Optimization

By Konstantinos E. Parsopoulos, Michael N. Vrahatis. Natural Computing, 2002

1,502 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/3e89a7a992402cd105cdefeaaf0bf2ad460c771c

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Citations in the corpus

Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 (2008, background): “Linear regression (LR) Yumurtaci and Asmaz (2004) energy forecasting.”
An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem (2009, methodology): “Parsopoulos and Vrahatis (2002) conducted the ﬁrst research on the particle swarm optimization method in multi-objective optimization problems.”
On the computation of all global minimizers through particle swarm optimization (2004, methodology): “Then, stretching is defined as [26], [64], [67], [68]”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “PARTICLE SWARM OPTIMIZATION (PSO) is now established as an efficient optimization algorithm for static functions in a variety of contexts [35].”
Adaptive sequentially space‐filling metamodeling applied in optimal water quantity allocation at basin scale (2010, methodology): “A set of values g1 = 5000, g2 = 0.5, and m = 10 −10 could be appropriate [Parsopoulos and Vrahatis, 2002].”
An improved particle swarm optimizer for mechanical design optimization problems (2004, methodology): “Parsopoulos and Vrahatis [22] incorporated a nonstationary multi-stage assignment penalty function into PSO.”
Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization (2005, background): “The reader is referred to [10]–[18] and the references mentioned therein for a detailed discussion of the basic concepts of the PSO algorithm and how it works.”
Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024, neutral): “This approach made locating the exact optimal solution for linear or convex non-linear optimization issues simpler [3].”
A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, neutral): “Parsopoulos and Vrahatis (2002b) applied PSO to integer programming by simply truncating real values to integers and claimed that the method does not significantly affect the search performance.”
A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization (2004, methodology): “Since our simulation model is computationally expensive, we run the PSO algorithms with a much lower number of simulations than the ones in [15,25,14], which makes convergence to a minimum less likely.”
Practical Genetic Algorithms (2005, background): “, 1983), particle swarm optimization (Parsopoulos and Vrahatis, 2002), ant colony optimization (Dorigo and Maria, 1997), and evolutionary algorithms (Schwefel, 1995).”
Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm (2017, background): “The PSO approach has two properties useful for this work: it is likely to avoid getting trapped in local minima and it is more robust to noisy objective-function calls [35].”
Particle swarm optimization algorithm: an overview (2017, methodology): “…to dynamically determine the inertia weight and learning factors, such as genetic algorithm (Yu et al. 2005), adaptive fuzzy algo-rithm (Juang et al. 2011), differential evolutionary algorithm (Parsopoulos and Vrahatis 2002b), adaptive critic design technology (Doctor and Venayagamoorthy 2005).”