Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

By A. K. Qin, Vicky Ling Huang, Ponnuthurai Nagaratnam Suganthan. 2009

3,412 citations recorded by the source corpus.

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.

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.”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, methodology): “The main motivation is that different strategies along with different parameter settings may be suitable to different problems [8].”
Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “Several effective parameter adaptation approaches have been proposed in previous studies [6,39,67].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “The books [54,150] on DE, research work on adaptive DE [55,56] and opposition-based DE [57,58] made the DE quite popular amongst researchers.”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “optimization [3,95], artificial bee colony (ABC) optimization [3,96], ant colony optimization (ACO) [3], genetic algorithms (GAs) [49] and the firefly algorithm [74] have been successfully applied for feature selection and reduction.”
Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, methodology): “Self-adaptive differential evolution algorithm for numerical optimization (SaDE) [8].”
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization (2012, background): “The performance of the MDE_pBX algorithm is compared with the following algorithms that include (in the order of appearance) two classical DE variants and four state-of-the-art ADE variants: 1) DE/current-to-best/1/bin with F = 0.8 and Cr = 0.9; 2) DE/rand/1/bin with F = 0.8 and Cr = 0.9; 3) JADE wi”
A social spider algorithm for global optimization (2015, methodology): “The convergence plots lead to the following observations: • The convergence speed of SSA in solving unimodal optimization problems is not as fast as CMA-ES, JADE, and SaDE.”
Differential Evolution for Prediction of Longitudinal Dispersion Coefficients in Natural Streams (2013, background): “Qin et al. (2009) proposed a self adaptive DE algorithm (SaDE), in which both trail vector generation strategies and the associated control parameter values were gradually self-adaptive by learning from their previous experiences when generating promising solutions.”
Single objective real-parameter optimization: Algorithm jSO (2017, methodology): “Control parameters F and CR are self-adaptive in SADE [14], jDE [15], JADE [16], etc.”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, background): “it is closely related to the convergence speed [19].”