Group Counseling Optimization: A Novel Approach

Conference paper

Abstract

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.

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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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