Comparer des méthodes

	Optimisation multi-objectif basée sur les agents	Optimisation multi-objectif
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Auteur d'origine	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Type	Simulation-driven multi-objective search	Optimization framework
Source fondatrice	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Apparentées	5	3
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 Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.