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Multi-Objective Optimization

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.

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Multi-Objective Optimization (MOO) — simultaneous optimization of two or more conflicting objective functions

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Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. · ISBN 9780471873396
Multi-objective optimization. Wikipedia. · URL

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Same method familyGenetic Algorithmmachine-suggested · Relational suggestion, not evidence.See alsoGOAL-PROGRAMMINGmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMixed-Integer Programmingmachine-suggested · Relational suggestion, not evidence.