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Multi-Objective Optimization — Simultaneously Optimizing Conflicting Goals
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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Sources
- Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
- Multi-objective optimization. Wikipedia. link ↗
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Referenced by
Agent-based multi-objective optimizationBayesian Goal ProgrammingBayesian Multi-Objective OptimizationBayesian NSGA-IIDeterministic Multi-Objective OptimizationMulti-objective agent-based modelingMulti-objective cellular automataMulti-objective discrete-event simulationMulti-objective dynamic programmingMulti-objective genetic algorithmMulti-objective goal programmingMulti-objective linear programmingMulti-objective Markov ModelMulti-objective microsimulationMulti-objective mixed-integer programmingMulti-objective particle swarm optimizationMulti-objective Queueing SimulationMulti-objective Scenario AnalysisMulti-objective sensitivity analysisMulti-objective simulated annealingMulti-objective system dynamicsNSGA-IIIPolicy Scenario Multi-Objective OptimizationRobust Multi-Objective OptimizationRobust NSGA-IIStochastic Multi-Objective Optimization