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={Duc Truong Pham and Marco 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… 
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References

SHOWING 1-10 OF 42 REFERENCES
The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems
TLDR
The Bees Algorithm is described in its basic formulation, and two recently introduced procedures that increase the speed and accuracy of the search are described, both in terms of learning accuracy and speed.
Nature-Inspired Metaheuristic Algorithms
TLDR
This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Comparison of Various Evolutionary and Memetic Algorithms
TLDR
This research aims to discover the properties, especially the efficiency (i.e. the speed of convergence) of particular evolutionary and memetic algorithms, by applying them on several numerical optimization benchmark functions and on machine learning problems.
Restart particle swarm optimization with velocity modulation: a scalability test
TLDR
This work incorporates two new mechanisms into the particle swarm optimization with the aim of enhancing its scalability, and shows that the proposal is scalable in all functions of the benchmark used, as well as numerically very competitive with regards to other compared optimizers.
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale
TLDR
A suite of benchmark functions for large-scale numerical optimization of metaheuristic optimization algorithms and a systematic evaluation platform is provided for comparing the scalability of different EAs.
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
  • Chia-Feng Juang
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2004
TLDR
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO.
...
...