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 self-belief 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 multi-objective 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.