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Агентно-ориентированный генетический алгоритм×Агентно-ориентированная многокритериальная оптимизация×
ОбластьИмитационное моделированиеИмитационное моделирование
СемействоProcess / pipelineProcess / pipeline
Год появления1990s1990s–2000s
Автор методаAdamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990sBonabeau, Dorigo, Theraulaz; Coello Coello et al.
ТипHybrid evolutionary-agent simulationSimulation-driven multi-objective search
Основополагающий источникAdamidis, 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. link ↗Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598
Другие названияABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GAABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO
Связанные55
СводкаAn 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.
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ScholarGateСравнение методов: Agent-based genetic algorithm · Agent-based multi-objective optimization. Получено 2026-06-15 из https://scholargate.app/ru/compare