手法を比較

	多目的最適化	Particle Swarm Optimization (PSO)
分野	シミュレーション	最適化
系統	Process / pipeline	Process / pipeline
提唱年	1896 (concept); 1989–2002 (evolutionary algorithms era)	1995
提唱者	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	—
種類	Optimization framework	Population-based metaheuristic / swarm intelligence
原典	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
別名	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
関連	3	6
概要	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.	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.