Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms

@article{Pham2014BenchmarkingAC,
  title={Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms},
  author={D. Pham and M. Castellani},
  journal={Soft Computing},
  year={2014},
  volume={18},
  pages={871-903}
}
This paper describes an experimental investigation into four nature-inspired population-based continuous optimisation methods: the Bees Algorithm, Evolutionary Algorithms, Particle Swarm Optimisation, and the Artificial Bee Colony algorithm. The aim of the proposed study is to understand and compare the specific capabilities of each optimisation algorithm. For each algorithm, thirty-two configurations covering different combinations of operators and learning parameters were examined. In order… Expand
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2
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References

SHOWING 1-10 OF 44 REFERENCES
Comparison among five evolutionary-based optimization algorithms
  • 1,191
  • PDF
Nature-Inspired Metaheuristic Algorithms
  • 3,212
Comparison of Various Evolutionary and Memetic Algorithms
  • 31
Restart particle swarm optimization with velocity modulation: a scalability test
  • 51
  • PDF
A novel numerical optimization algorithm inspired from weed colonization
  • 655
...
1
2
3
4
5
...