Comparar métodos

	Algoritmo Genético Basado en Agentes	Optimización Multi-objetivo Basada en Agentes
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s	1990s–2000s
Autor original	Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.
Tipo	Hybrid evolutionary-agent simulation	Simulation-driven multi-objective search
Fuente seminal	Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE.	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598
Alias	ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO
Relacionados	5	5
Resumen	An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence.	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.