Comparer des méthodes

	Optimisation multi-objectif basée sur les agents	Optimisation par essaims particulaires multi-objectif (MOPSO)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	2004
Auteur d'origine	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Type	Simulation-driven multi-objective search	Population-based swarm metaheuristic
Source fondatrice	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598	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	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Apparentées	5	5
Résumé	Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations.	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.