Multi-objective Tabu Search
Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa. · URL
- Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206. · DOI 10.1287/ijoc.1.3.190
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