Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

By Yong Wang, Zixing Cai, Qingfu Zhang. 2011

1,351 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, methodology): “This section also provides a comparative result of training and testing of different FLANNS using PSO, HMRPSO, and DE (Mohapatra et al., 2012; Qin et al., 2008; Wang et al., 2011) based learning for predicting ﬁnancial time series data.”
Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm (2013, methodology): “Popular adaptive DEs include SaDE [31], FADE [32], jDE [33], JADE [34], EPSDE [35], and CoDE [36], etc.”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “Thus, different mutation strategies and control parameters are employed for different issues [54], [56], [57], [58].”
Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, methodology): “It is also worth noting that some other methods consider the so-called multioperators, such as the ensemble-based method [25] and the composite operators [26].”
Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm (2016, methodology): “Among the compared algorithms, ABC [16,26], GSO [18], and GSA[20,27] are recently published algorithms while CLPSO [9] and CoDE [28] are competitive variants of PSO and DE.”
Differential Evolution With Ranking-Based Mutation Operators (2013, background): “1 [25] CoDE, rank-CoDE Np = 30 [22] DEGL, rank-DEGL Np = 10×D,Cr = 0.”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, methodology): “[23] investigated whether the performance of DE can be improved by combining several trial vector generation strategies with several different control parameter settings, which exhibit different characterizes, and proposed a composite DE, named CoDE.”
Recent advances in differential evolution - An updated survey (2016, methodology): “However, this algorithm can be computationally costlier, especially on large scale problem instances, due to the involvement of two global optimizers: DE and HS. Wang et al. [197] developed a Composite DE (CoDE) algorithm where a trial vector is chosen from a set of candidates generated by using dif”
Event-Triggering Load Frequency Control for Multiarea Power Systems With Communication Delays (2016, methodology): “Recently, there have been some novel methods to deal with time-varying delay, such as the input–output approach [41] or combining multiobjective optimization with differential evolution [42]–[44] or neural networks [45]–[47]; these methods will benefit from the research of event-triggering LFC of mu”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “On the other hand, A large F value improves the exploration capability of the algorithm that can make the mutant vectors distribute widely in the search space and can increase the diversity of the population [21].”
Improving the search performance of SHADE using linear population size reduction (2014, methodology): “1 [9], CoDE [18], EPSDE [6], SaDE [4], JADE [5], and dynNP-jDE [15] on the CEC2014 benchmarks.”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, methodology): “Therefore, in order to further evaluate the performance of R cr -IJADE, it is compared with ﬁve advanced DE variants ( i.e. , jDE [32], SaDE [33], CoDE [36], DEGL [35], and JADE [34]) to solve the parameter extraction problems of solar cell models.”