JADE: Adaptive Differential Evolution With Optional External Archive

By Jingqiao Zhang, Arthur C. Sanderson. IEEE Transactions on Evolutionary Computation, 2009

3,449 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/4b7595bc5e1b8b04c249c3272b0566d4432faf2c

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Citations in the corpus

Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “New variants of DE have been proposed to improve its performance, such as EPSDE [20], SHADE [21], SaDE [22], jDE [23], and JADE [24].”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “The subcomponent optimization algorithm is changed among JADE and SaNSDE when the current optimization algorithm cannot any improvement in the fitness value.”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, methodology): “Rcr-IJADE is an improved version of JADE (Zhang and Sanderson, 2009), where two improvements are synergized.”
Recent advances in differential evolution - An updated survey (2016, methodology): “JADE is a powerful DE-variant that uses a control parameter adaptation strategy based on updating the parameters of the probability distributions from which values of F and Cr are sampled.”
Differential Evolution for Prediction of Longitudinal Dispersion Coefficients in Natural Streams (2013, background): “Moreover, DE has been proved to be quite efficient when solving real-world problems (Brest et al. 2006; Rahnamayan et al. 2008; Qin et al. 2009; Li et al. 2011; Li and Yin 2012a, b, 2013, 2012a, b; Neri and Tirronen 2009; Zhang and Sanderson 2009; Qin et al. 2010; Vasan and Raju 2007).”
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, background): “[7] Self-adaptive DE (SaDE) Qin and Suganthan [54] Adaptive DE with optional external archive (JADE) Zhang and Sanderson [74] F.”
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).”
Differential Evolution: A Survey of the State-of-the-Art (2011, background): “Although a powerful variant of ES, known as restart covariance matrix adaptation ES (CMA-ES) [S232, S233], yielded better results than classical and self-adaptive DE, later on many improved DE variants [like improved SaDE [76], jDE [10], opposition-based DE (ODE) [82], DE with global and local neigh”
Success-history based parameter adaptation for Differential Evolution (2013, methodology): “(20) is the maximum value of the range for p suggested by Zhang and Sanderson [4].”
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, methodology): “Apart from the aforementioned three variants, in order to show that the parameter adaptation schemes of MDE_pBX are superior than that of JADE, we also present here the results of the MDE_pBX algorithm where the original parameter adaptation schemes of MDE_pBX are replaced with that of JADE.”
Backtracking Search Optimization Algorithm for numerical optimization problems (2013, methodology): “BSA has a random mutation strategy that uses only one direction individual for each target individual, in contrast with many genetic algorithms such as DE and its derivatives JDE, JADE and SADE.”
A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “The proposed SL-PSO has been tested on 47 functions, including 12 widely used basic test functions [91,4,97,96,8] (f 1 to f 12, refer to Table 1), 28 functions taken from the whole test suite for CEC’13 special session on real-parameter optimization (f 13 to f 40, refer to [47]), and 7 functions tak”
Evolutionary Computation Meets Machine Learning: A Survey (2011, background): “The idea of using statistical methods for parameter adaptation are also reported in the self-adaptive DE proposed by Qin et al. [55] and in the adaptive DE proposed by Zhang and Sanderson [56].”
A social spider algorithm for global optimization (2015, methodology): “For DE variants, we select JADE [40] and SaDE [32] for comparison due to their excellent performance demonstrated in the CEC 2013 competition.”