Порівняння методів

	Агентно-орієнтована багатоцільова оптимізація	Стохастична багатоцільова оптимізація
Галузь	Імітаційне моделювання	Імітаційне моделювання
Родина	Process / pipeline	Process / pipeline
Рік появи	1990s–2000s	1990s–2000s
Автор методу	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Тип	Simulation-driven multi-objective search	Stochastic metaheuristic optimization
Основоположне джерело	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
Інші назви	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Пов'язані	5	5
Підсумок	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.	Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.