The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Improving the search performance of SHADE using linear population size reduction
Abstract
SHADE is an adaptive DE which incorporates success-history based parameter adaptation and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function. We evaluated the performance of L-SHADE on CEC2014 benchmarks and compared its search performance with state-of-the-art DE algorithms, as well as the state-of-the-art restart CMA-ES variants. The experimental results show that L-SHADE is quite competitive with state-of-the-art evolutionary algorithms.
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Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
JADE: Adaptive Differential Evolution With Optional External Archive
Zhang, J., & Sanderson, A. C. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2014613 · Cited by 3,739
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.927706 · Cited by 3,614
Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.872133 · Cited by 3,077
(2005) · Cited by 2,565
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
Wang, Y., Cai, Z., & Zhang, Q. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2087271 · Cited by 1,432
Success-history based parameter adaptation for Differential Evolution
Tanabe, R., & Fukunaga, A. (2013) · 2013 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2013.6557555 · Cited by 1,367
Self-citationDifferential evolution algorithm with ensemble of parameters and mutation strategies
Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2010.04.024 · Cited by 1,362
A Restart CMA Evolution Strategy With Increasing Population Size
Auger, A., & Hansen, N. (2005) · 2005 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2005.1554902 · Cited by 952
Cited by
Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems
Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2021.08.013 · Cited by 1,296
Bio-inspired computation: Where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 593
Awad, N. H., Ali, M. Z., & Suganthan, P. N. (2017) · 2017 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2017.7969336 · Cited by 473
Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application
Houssein, E. H., Gad, A. G., Hussain, K., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100868 · Cited by 466
RetractedEnsemble of differential evolution variants
Wu, G., Shen, X., Li, H., Chen, H., Lin, A., & Suganthan, P. N. (2018) · Information Sciences · doi:10.1016/j.ins.2017.09.053 · Cited by 386
Metadata
- Authors
- Tanabe, R., & Fukunaga, A. S.
- Venue
- 2014 IEEE Congress on Evolutionary Computation (CEC)
- Publisher
- IEEE
- Published
- 2014-07
- Pages
- 1658-1665
- OpenAlex
- W2042253843
- Cited by
- 1,625
- References
- 21
- FWCI
- 29.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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