A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems

By J. Vesterstrom, René Thomsen. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), 2004

1,331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ea3cc448712614733bd80d1392478105ff6cf98c

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Citations in the corpus

Large scale evolutionary optimization using cooperative coevolution (2008, methodology): “The proposed EACC-G has been designed to decompose a nonseparable problem following the above principle.”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “In the standard DE algorithm, five different mutation strategies can be used with one of the two different crossover methods (Shahryar et al. 2008; Das and Suganthan 2009; Kaelo and Ali 2006; Swagatam et al. 2009; Janez et al. 2007; Price et al. 2005; Vesterstrom and Thomsen 2004; Bin et al. 2010)”
Quasi-oppositional Differential Evolution (2007, background): “According to frequently reported comprehensive studies [8], [22], DE outperforms many other optimization methods in terms of convergence speed and robustness over common benchmark functions and real-world problems.”
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2009, methodology): “Here, “DE/rand/1/bin” is chosen because it employs a most commonly used trial vector generation strategy, with three commonly suggested sets of control parameters: 1) ; 2) ; and 3) [15], [21], [33].”
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, methodology): “However, time series data in real-world settings commonly have nonlinear features under a new economic era (Lee & Tong, 2012; Liu & Wang, 2014a, 2014b; Matias & Reboredo, 2012). Consequently, ARIMA may be unsuitable for most nonlinear real-world problems (Khashei, Bijari, & Ardali, 2009; Zhang, Patu”
Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, methodology): “It has outperformed many other algorithms such as particle swarm optimization in numerous benchmark problems [93] and has been amongst frontier global optimization methods in numerous completions and contests [94–96].”
Easy over hard: a case study on deep learning (2017, background): “For example, Vesterstrom and omsen [66] found DE to be competitive with particle swarm”
A comparative study of Artificial Bee Colony algorithm (2009, background): “Inertia weight, which determines how the previous velocity of the particle influences the velocity in the next iteration , was 0.6 as recommended in [44].”
Oppositional biogeography-based optimization (2009, background): “More information on these functions can be found in [1], [5], [12], [13].”
Spider Monkey Optimization algorithm for numerical optimization (2014, methodology): “Previous research [7,17,28,39] have shown that algorithms based on Swarm Intelligence have great potential to find a near optimal solution of real world optimiza-”
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers (2024, neutral): “Among these, evolutionary algorithms (EAs) stand out as they are simple and efficient, as well as suitable for discrete prompt optimization (Storn & Price, 1997; Brest et al., 2006; Zhang & Sanderson, 2009; Vesterstrom & Thomsen, 2004).”