Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems
By Janez Brest, S. Greiner, Borko Boskovic, Marjan Mernik, V. Zumer. IEEE Transactions on Evolutionary Computation, 2006
3,048 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/5a4c3b779dd63f4c81fc52fd75645113cdcca299
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In Artificial Intelligence
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
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- Ran Cheng
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In Computational Theory and Mathematics
- Ponnuthurai Nagaratnam Suganthan
- Swagatam Das
- Yaochu Jin
- Pinar Çivicioglu
- Ran Cheng
- Zhihua Cai
- Wenyin Gong
- Ferrante Neri
Citations in the corpus
- Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition (2014, methodology): “Some other AOS methods based on probabilities can also be found in the literature, such as the ones used in the SaDE [17], JADE [18], and jDE [19] algorithms.”
- JADE: Adaptive Differential Evolution With Optional External Archive (2009, methodology): “3The results of jDE reported in [15] are slightly different from but consistent with those in Table IV obtained in our independent experiments.”
- Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, methodology): “DE/rand/1/bin DE/targetto-best/1/bin DE/rand/1/ either-or SADE [28] NSDE [31] DEGL/SAW”
- Improving the search performance of SHADE using linear population size reduction (2014, background): “Since this is a significant problem in practice, adaptive mechanisms for adjusting the control parameters on-line during the search process have been studied by many researchers [3]–[6].”
- Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, background): “Second, adaptive parameter control strategies should be investigated; such strategies have proved beneficial for the performance of EAs [60]– [62].”
- 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.”
- Seeker Optimization Algorithm for Optimal Reactive Power Dispatch (2009, methodology): “Twelve benchmark functions (listed in Table I) are chosen from [40] to test the SOA with comparison of PSO-w (PSO with adaptive inertia weight) [41], PSO-cf (PSO with constriction factor) [42], CLPSO (comprehensive learning particle swarm optimizer) [43], the original DE [17], SACP-DE (DE with selfa”
- Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “New variants of DE have been proposed to improve its performance, such as EPSDE [20], SHADE [21], SaDE [22], jDE [23], and JADE [24].”
- Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters (2011, methodology): “[16] proposed a self-adaptive DE (jDE), in which both F and Cr are applied at individual level.”