Evaluating the performance of Group Counseling Optimizer on CEC 2014 problems for Computational Expensive Optimization

Group Counseling Optimizer (GCO) is a recently proposed population-based metaheuristics that simulates the ability of human beings to solve problems through counseling within a group. It is motivated by the fact that the human thinking ability is often predicted to be the most rational. This research article examines the performance of GCO on the benchmark test suite designed for the CEC 2014 Competition for Computational Expensive Optimization. Experimental results on 24 black-box optimization problems (8 test problems with 10, 20 and 30 dimensions) have been tabulated along with the algorithm complexity metrics. Additionally we investigate the parametric behavior of GCO based on these test instances.