Improving the search performance of SHADE using linear population size reduction

By Ryoji Tanabe, Alex S. Fukunaga. IEEE Congress on Evolutionary Computation, 2014

1,554 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9f358a4590d987ab448b4544193fd203a9ae44ce

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Citations in the corpus

Recent advances in differential evolution - An updated survey (2016, methodology): “Tanabe and Fukunaga [180] further improved the SHADE algorithm by using the linear population size reduction and called this variant as L-SHADE.”
LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems (2017, methodology): “Tanabe and Fukunaga [18] further improved the SHADE algorithm by using the linear population size reduction and called this variants LSHADE.”
Bio-inspired computation: Where we stand and what's next (2019, background): “This is a simple strategy that can render very good results [304, 305].”
Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, methodology): “LSHADE algorithm was ranked the winner of the single objective real parameter competition of the CEC’14 and demonstrated a very good performance when solving such benchmarks [12].”
Boosted binary Harris hawks optimizer and feature selection (2020, methodology): “These evolutionary algorithms contain: JADE [106], SaDE [107], jDE [108], CoDE [109], SHADE [110], ALCPSO [111], CLPSO [112], BLPSO [113], HCLPSO [114], EPSO [115], and HPSO_TVAC [116].”
Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts (2021, neutral): “…Wu, Mallipeddi, Suganthan, Wang, & Chen, 2015), SPS_L_SHADE_EIG (Guo, Tsai, Yang, & Hsu, 2015), LSHADE_cnEpSi (Awad, Ali, & Suganthan, 2017), SHADE (Tanabe & Fukunaga, 2014), SADE (Qin, Huang, & Suganthan, 2009), LSHADE (Tanabe & Fukunaga, 2014), JDE (Brest, Greiner, Boskovic, Mernik, & Zumer,…”
An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “We used the Java source code of L-SHADE provided by the authors of [55].”
Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, methodology): “The inspiration of the dynamic sine-cosine mechanism is from the switch of sine and cosine functions in the sine cosine algorithm (SCA) (Mirjalili, 2016) and the mutation strategy of linear population size reduction adaptive differential evolution (LSHADE) (Tanabe and Fukunaga, 2014).”
Single objective real-parameter optimization: Algorithm jSO (2017, methodology): “In this section L-SHADE [6] and iL-SHADE are presented briefly.”
The Social Engineering Optimizer (SEO) (2018, positive): “…et al., 2018; Hajiaghaei-Keshteli and Fathollahi Fard, 2018) and also a state of art Optimizer called Linear-Success-History based Adaptive of Differential Evolution (L-SHADE) as an improved version of DE (Tanabe and Fukunaga, 2014); are utilized to compare with the four versions of SEO proposed.”
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems (2022, methodology): “For performance validation of HBA, the results are compared with ten state-of-the-art optimization algorithms: SA [40], PSO [47], CMA-ES [12], L-SHADE [50], MFO [42], EHO [51], WOA [43], GOA [49], TEO [37], and HHO [19].”