Single-objective and multi-objective optimization using the HUMANT algorithm
Marko Mladineo, Ivica Veža, Nikola Gjeldum · Croatian Operational Research Review · 2015
When facing a real world, optimization problems mainly become multiobjective i.e. they have several criteria of excellence.A multi-criteria problem submitted for multi-criteria evaluation is a complex problem, as usually there is no optimal solution, and no alternative is the best one according to all criteria.However, if a metaheuristic algorithm is combined with a Multi-Criteria Decision-Making method then, instead of submitting all solutions, only near-optimal solutions are submitted for multi-criteria evaluation, i.e. compared and ranked using a priori decision-maker preferences.It is called an a priori approach to multi-objective optimization.This paper presents this approach using a specially designed HUMANT (HUManoid ANT) algorithm derived from Ant Colony Optimization and the PROMETHEE method.The preliminary results of this optimization algorithm are presented for the Single-Objective Traveling Salesman Problem (TSP), Shortest Path Problem (SPP) and the Multi-Objective Partner Selection Problem (PSP).Additionally, the multi-objective approach of the HUMANT algorithm to single-objective optimization problems is presented using the Shortest Path Problem (SPP).