เปรียบเทียบวิธี

	การหาค่าเหมาะสมที่สุดแบบหลายวัตถุประสงค์	Genetic Algorithm	การโปรแกรมจำนวนเต็มผสม
สาขาวิชา	การจำลอง	การหาค่าเหมาะที่สุด	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	1896 (concept); 1989–2002 (evolutionary algorithms era)	1975	1958–1960
ผู้ริเริ่ม	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)
ประเภท	Optimization framework	Population-based metaheuristic	Mathematical optimization
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
ที่เกี่ยวข้อง	3	5	6
สรุป	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.