Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafutaji wa Tabu Wenye Malengo Mengi (MOTS)× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1997 | 1984 |
| Mwanzilishi≠ | Hansen, M. P.; building on Glover (1989) Tabu Search | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Aina≠ | Metaheuristic multi-objective optimization | Population-based evolutionary optimizer |
| Chanzo asilia≠ | 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. link ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Majina mbadala | MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | 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. | A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among. |
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