เปรียบเทียบวิธี

	การปรับให้เหมาะสมด้วยวิธีฝูงมดแบบเอเจนต์	Ant Colony Optimization
สาขาวิชา	การจำลอง	การหาค่าเหมาะที่สุด
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1992-2004	1992 (foundational thesis); 1997 (Ant Colony System formalization)
ผู้ริเริ่ม	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	—
ประเภท	Metaheuristic optimization — agent-based swarm simulation	Metaheuristic — swarm intelligence
แหล่งต้นตำรับ	Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192	Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.
ชื่อเรียกอื่น	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
ที่เกี่ยวข้อง	5	5
สรุป	Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination.	Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.