Adapting the artificial bee colony metaheuristic to solve multi-objective problems.

In this paper, an artificial bee colony (ABC) metaheuristic is adapted to find Pareto optimal set solutions for goal programming problems. The proposed algorithm is named weighted goal programming artificial bee colony (WGP-ABC). This WGP-ABC is personalised to support the MOO by means of a weighted sum formulation for the objective function: solving several scalarisations of the objective function according to a weight vector with non-negative components. The efficiency of the proposed approach is demonstrated by nonlinear engineering design problems. In all problems, multiple solutions to the goal programming problem are found in short computational time using very few user-defined parameters.