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	Multi-objective Tabu Search (MOTS)	Multi-Objective Particle Swarm Optimization (MOPSO)
Ämnesområde	Simulering	Simulering
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1997	2004
Upphovsperson	Hansen, M. P.; building on Glover (1989) Tabu Search	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Typ	Metaheuristic multi-objective optimization	Population-based swarm metaheuristic
Ursprungskälla	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.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
Alias	MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Närliggande	5	5
Sammanfattning	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.	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.