手法を比較

	多目的最適化	遺伝的アルゴリズム	ゴールプログラミング
分野	シミュレーション	最適化	意思決定
系統	Process / pipeline	Process / pipeline	MCDM
提唱年	1896 (concept); 1989–2002 (evolutionary algorithms era)	1975	1955
提唱者	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	John Henry Holland	Charnes, A., Cooper, W. W.
種類	Optimization framework	Population-based metaheuristic	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation
原典	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science
別名	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	—
関連	3	5	8
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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.