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

  title={Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms},
  author={Duc Truong Pham and Marco Castellani},
  journal={Soft Computing},
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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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
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.