Сравнение методов

	Агентно-ориентированный генетический алгоритм	Оптимизация роем частиц (PSO)
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s	1995
Автор метода	Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s	—
Тип	Hybrid evolutionary-agent simulation	Population-based metaheuristic / swarm intelligence
Основополагающий источник	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Другие названия	ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Связанные	5	6
Сводка	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.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.