In this paper, a new population-based optimization algorithm – which we call a group counseling optimizer (GCO) – is developed. Instead of mimicking the behavior of living organisms such as birds, fish, ants, and bees, we emulate the behavior of human beings in life problem solving through counseling within a group. This is motivated by the fact that the human's thinking is often predicted to be the most reasonable and influential. The inspiration radiates from the various striking points of analogy between group counseling and population-based optimization which we have discovered, as elucidated in Section 2. The algorithm is tested using seven unrotated benchmark functions and five rotated ones. Further, a comparison is made with the comprehensive learning particle swarm optimizer (CLPSO) which outperforms many other variants of the particle swarm optimizer. Using new eight composition benchmark functions, another comparison is made with the BI-population covariance matrix adaptation evolution strategy with alternative restart strategy (NBIPOP-aCMA-ES) which is the winner of the competition on real-parameter single objective optimization at IEEE CEC-2013. The results are all highly promising, demonstrating the soundness and efficacy of the proposed approach. GCO is applied to real-world application which is spacecraft trajectory design problem. Also, the results show that GCO outperforms well-known optimizers. 
Group counseling optimization