Single-objective and multi-objective optimization using the HUMANT algorithm
| Autor: | Marko Mladineo, Ivica Veža, Nikola Gjeldum |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Croatian Operational Research Review, Vol 6, Iss 2, Pp 459-473 (2015) |
| Druh dokumentu: | article |
| ISSN: | 1848-0225 1848-9931 |
| DOI: | 10.17535/crorr.2015.0035 |
| Popis: | 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). |
| Databáze: | Directory of Open Access Journals |
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