A comprehensive study of cuckoo-inspired algorithms

Faculty Computer Science Year: 2018
Type of Publication: ZU Hosted Pages:
Authors:
Laila Abdel Fattah Shawqi Ibrahim
Journal: Neural Computing and Applications Springer‏ Volume:
Keywords : , comprehensive study , cuckoo-inspired algorithms    
Abstract:
Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo searc
    
     
 
       

Author Related Publications

  • Laila Abdel Fattah Shawqi Ibrahim, "Elite opposition-flower pollination algorithm for quadratic assignment problem", IOS press, 2017 More
  • Laila Abdel Fattah Shawqi Ibrahim, "A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems", emerald insight, 2017 More
  • Laila Abdel Fattah Shawqi Ibrahim, "Metaheuristic Algorithms: A Comprehensive Review", Elsevier‏, 2018 More
  • Laila Abdel Fattah Shawqi Ibrahim, "An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems", Springer‏, 2018 More
  • Laila Abdel Fattah Shawqi Ibrahim, "Grid quorum‐based spatial coverage in mobile wireless sensor networks using nature‐inspired firefly algorithm", John Wiley & Sons, 2019 More

Department Related Publications

  • Mohammed Abdel Basset Metwally Attia, "Discrete greedy flower pollination algorithm for spherical traveling salesman problem", Springer, 2019 More
  • Mohammed Abdel Basset Metwally Attia, "A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems", Natural Sciences Publishing Cor., 2014 More
  • Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015 More
  • Saber Mohamed, "An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems", IEEE, 2013 More
  • Saber Mohamed, "Differential Evolution with Dynamic Parameters Selection for Optimization Problems", IEEE, 2014 More
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