Process / pipelineSimulation / optimization
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
- Multi-objective optimization. Wikipedia. link ↗
Related methods
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