A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible.
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Computer Science(all)
- Industrial and Manufacturing Engineering
- Modeling and Simulation
- Management Science and Operations Research