Group Counseling Optimization for multi-objective functions

Group Counseling Optimizer (GCO) is a new heuristic inspired by human behavior in problem solving during counseling within a group. GCO has been found to be successful in case of single-objective optimization problems, but so far it has not been extended to deal with multi-objective optimization problems. In this paper, a Pareto dominance based GCO technique is presented in order to allow this approach to handle multi-objective optimization problems. In order to compute change in decision for each individual, we also incorporate a selfbelief counseling probability operator in the original GCO algorithm that enriches the exploratory capabilities of our algorithm. The proposed Multi-objective Group Counseling Optimizer (MOGCO) is tested using several standard benchmark functions and metrics taken from the literature for multiobjective optimization. The results of our experiments indicate that the approach is highly competitive and can be considered as a viable alternative to solve multi-objective optimization problems.