手法を比較

	エージェントベース多目的最適化	多目的粒子群最適化（MOPSO）
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	1990s–2000s	2004
提唱者	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
種類	Simulation-driven multi-objective search	Population-based swarm metaheuristic
原典	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
別名	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
関連	5	5
概要	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.	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.