Success-history based parameter adaptation for Differential Evolution

By Ryoji Tanabe, Alex S. Fukunaga. IEEE Congress on Evolutionary Computation, 2013

1,310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8fba8f7209e18be93e225fb95e6422b8bd5522d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Seyedali Mirjalili
4. Xin-She Yang
5. Pinar Çivicioglu
6. Sankha Subhra Mullick
7. Seyed Mohammad Mirjalili
8. Zhi-hui Zhan

In Computational Theory and Mathematics

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Xin-She Yang
4. Seyedali Mirjalili
5. Sankha Subhra Mullick
6. Seyed Mohammad Mirjalili
7. Pinar Çivicioglu
8. Francisco Herrera

Citations in the corpus

• 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.”
• Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “MPEDE was compared with six other state-of-the-art DE variants including JADE [67], jDE [6], SaDE [39], EPSDE [33], CoDE [56] and SHADE [50].”
• Equilibrium optimizer: A novel optimization algorithm (2020, neutral): “…Matrix Adaptation (CMA-ES) [13], Success-History Based Parameter Adaptation Differential Evolution (SHADE) (one of the CEC 2013 competitors) [27], and SHADE with linear population size reduction hybridized with semi-parameter adaptation of CMA-ES (LSHADE-SPACMA) (one of the CEC 2017…”
• 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.”
• Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems (2017, methodology): “As shown in this table, LSHADE-cnEpSin clearly has the best overall performance among all the variants and significantly improves upon the performance of L-SHADE and UMOEAsII algorithms.”
• An improved differential evolution algorithm and its application in optimization problem (2021, methodology): “In order to prove the optimization performance of NBOLDE, the NBOLDE is compared with some existing As can be seen from Tables 3, 4, and 5, for single peak functions, which is less difficult to solve, the ODE, JADE, SaDE and NBOLDE can achieve higher convergence accuracy than the DE.”
• Bio-inspired computation: Where we stand and what's next (2019, background): “CMA-ES [8], IPOP-CMA-ES [9] or the more recent SHADE approach [10]).”
• A survey on evolutionary computation for complex continuous optimization (2021, methodology): “Among them, both IHDELS and SHADE-ILS improve the performance by accelerating the convergence speed in solving LSOP via iteratively embedding different local search strategies.”