Single-objective and multi-objective optimization using the HUMANT algorithm
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Croatian Operational Research Society
2015-10-01
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| Series: | Croatian Operational Research Review |
| Subjects: | |
| Online Access: | http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=218180 |
| Summary: | 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). |
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| ISSN: | 1848-0225 1848-9931 |