Group Counseling Optimization: A Novel Approach

  • M. A. Eita
  • M. M. Fahmy
Conference paper


A new population-based search algorithm, which we call Group Counseling Optimizer (GCO), is presented. It mimics the group counseling behavior of humans in solving their problems. The algorithm is tested using seven known benchmark functions: Sphere, Rosenbrock, Griewank, Rastrigin, Ackley, Weierstrass, and Schwefel functions. A comparison is made with the recently published comprehensive learning particle swarm optimizer (CLPSO). The results demonstrate the efficiency and robustness of the proposed algorithm.


Genetic Algorithm Differential Evolution Candidate Solution Memetic Algorithm Benchmark Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  1. 1.Faculty of Engineering, University of TantaTantaEGYPT

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