Сравнение методов

	Многокритериальная оптимизация	Генетический алгоритм	Смешанное целочисленное программирование
Область	Имитационное моделирование	Оптимизация	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1896 (concept); 1989–2002 (evolutionary algorithms era)	1975	1958–1960
Автор метода	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	John Henry Holland	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Тип	Optimization framework	Population-based metaheuristic	Mathematical optimization
Основополагающий источник	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.	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Другие названия	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Связанные	3	5	6
Сводка	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.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.