A Fuzzy Adaptive Differential Evolution Algorithm

By Junhong Liu, Jouni Lampinen. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings., 2002

938 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/ca7605b6625b973f6ac626147ed2923d7c1ec534

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Citations in the corpus

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “Liu and Lampinen [20] proposed a Fuzzy Adaptive DE (FADE), which employs fuzzy logic controllers to adapt the mutation and crossover control parameters.”
Differential evolution with multi-population based ensemble of mutation strategies (2016, methodology): “Some parameter adaptation strategies were proposed, such as linear reduction [13], random sampling [8], fuzzy logic control [30], simulated annealing based parameter control [22] and population diversity based parameter control [65].”
Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm (2013, methodology): “In [32], Liu et al.”
Opposition versus randomness in soft computing techniques (2008, neutral): “5 [13,29,30,32,38,40] Crossover probability constant (Cr) 0.”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, background): “Previous studies [38, 32] indicate that the parameter setting is crucial to the performance of DE.”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “In addition, though DE is particularly simple to work with, having only a few control parameters, choice of these parameters is often critical for the performance of DE [24].”
A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm (2004, neutral): “It has been applied to several engineering problems in different areas [4, 5, 6, 7].”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, background): “Liu and Lampinen [17] designed a fuzzy adaptive DE (FADE) based on fuzzy logic controller.”
Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, background): “tion over continuous spaces [2], [16], [17].”
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, background): “Liu and Lampinen [15] proposed a fuzzy adaptive DE, which uses a fuzzy knowledgebased system to dynamically adjust F and Cr.”
Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Kukkonen and Lampinen extended DE/rand/1/bin to solve multiobjective optimization problems in their approach called generalized differential evolution (GDE).”
JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “The fuzzy adaptive differential evolution (FADE), which was introduced by Liu and Lampinen [11], is a new variant of DE that uses fuzzy logic controllers to adapt the con-trol parameters F i and C R i for the mutation and crossover operations.”
Opposition-Based Differential Evolution (2008, methodology): “We have compared ODE with the FADE method of Liu and Lampinen [13].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…et al. 2000; Juang 2004), Differential evolution algorithm (DE) (Storn and Price 1997; Ferrante and Ville 2010; Price and Storn 1997; Storn 1999; Liu and Lampinen 2005; Ali and Torn 2004; Shahryar et al. 2008; Das and Suganthan 2009; Kaelo and Ali 2006; Swagatam et al. 2009; Janez et al. 2007),…”