Self-adaptive differential evolution algorithm for numerical optimization

By A. K. Qin, Ponnuthurai Nagaratnam Suganthan. IEEE Congress on Evolutionary Computation, 2005

1,283 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7e19698880bb57393715182e9c87e0b72b9a570a

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Citations in the corpus

Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, methodology): “SADE algorithm is based on adaptive use of the mutation strategies, which are used in the DE algorithm (Qin and Suganthan, 2005; Brest et al., 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.”
A comparative study of differential evolution variants for global optimization (2006, background): “Qin and Suganthan [15] proposed a Self-adaptive DE.”
Population size reduction for the differential evolution algorithm (2008, background): “Qin and Suganthan in [23] proposed the Self-adaptive Differential Evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR do not require pre-defining.”
Backtracking Search Optimization Algorithm for numerical optimization problems (2013, methodology): “BSA’s mutation and crossover mechanisms are different from those of DE and its advanced versions JDE, JADE and SADE.”
Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, methodology): “Moreover, they represent a broad array of optimization techniques, including single search operator (CoEVO, DE, EDA, G-CMA-ES, L-CMA-ES, K-PCX, SPC-PNX), and multimethod memetic (BLX-GL50, BLX-MA, DMS-L-PSO), and self-adaptive (L-SaDE) evolutionary algorithms.”
A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization (2009, neutral): “Average error rate obtained in CEC'2005 Special Session in dimension 10 A l g o r i t h m f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8 f 9 BLX-GL50 10 −9 10 −9 5.705 · 10 2 10 −9 10 −9 099 · 10 4.02 Algorithm f10 f11 f12 f13 f14 f15 f16 f17 f18 BLX-GL50 4.975 2.334 4.069 · 10 2 7.498 · 10 −1 2.172 4 · 10 2 9.349”
JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “SaDE [13] is proposed by Qin and Suganthan to simultaneously implement two mutation strategies “DE/rand/1” and “DE/current-to-best/1.””
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “In the area of DE, the SaDe algorithm by Qin and Suganthan [176] combines adaptive control of the amplification factor and crossover rate with adaptive selection between two operators.”
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, background): “[43]; and 8) the differential evolution algorithm with random”
One Pixel Attack for Fooling Deep Neural Networks (2017, methodology): “The DE utilized in this paper belongs to a big class of algorithms called evolutionary strategies [47] which includes other variants, such as adaptive DE [48] and covariance matrix”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “First DE versions harnessing this design principle, like SaDE [312] or JADE [313], used only a mean value for each parameter, which is adapted along the run.”
Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm (2013, methodology): “In [31], Qin et al.”
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.”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “The Congress on Evolutionary Computation 2005 (CEC 2005) (Suganthan et al. 2005) provided a common platform for comparison of the performance of different algorithms by specifying a common termination criterion, size of problem, initialization scheme, etc .”
Seeker Optimization Algorithm for Optimal Reactive Power Dispatch (2009, methodology): “[44] proposed a self-adaptive differential evolution (SaDE) where the choice of learning strategy and the two control parameters and CR are not required to be pre-specified.”
Self-adaptive learning based particle swarm optimization (2011, methodology): “In the previous research of optimization techniques, the scheme of simultaneously using multiple offspring creation strategies from one or more different algorithms has been studied by a number of works [33,34,43,44,46,54,58,59].”