Single-objective and multi-objective optimization using the HUMANT algorithm

• Main Authors: Marko Mladineo, Ivica Veža, Nikola Gjeldum
• Format: Article
• Language: English
• Published: Croatian Operational Research Society 2015-10-01
• Series: Croatian Operational Research Review
• Subjects: single-objective optimization; multi-objective optimization; HUMANT algorithm; PROMETHEE method; ant colony optimization
• Online Access: http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=218180

When facing a real world, optimization problems mainly become multi-objective 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).