手法を比較

	Multi-Objective Agent-Based Modeling	多目的遺伝的アルゴリズム（MOGA）
分野	シミュレーション	シミュレーション
系統	Process / pipeline	Process / pipeline
提唱年	2001-2006	1984
提唱者	Deb, K.; Tesfatsion, L. et al.	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
種類	Simulation-optimization hybrid	Population-based evolutionary optimizer
原典	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
別名	MO-ABM, Multi-objective ABM, Pareto-based agent-based modeling, Multi-objective agent simulation	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
関連	4	4
概要	Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals.	A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.