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* Constructing a single aggregate objective function (AOF)
This is perhaps the most intuitive approach to solving the multiobjective problem. The basic idea is to combine all of the objective functions into a single functional form, called the AOF. A well-known combination is the weighted linear sum of the objectives. One specifies scalar weights for each objective to be optimized, and then combines them into a single function that can be solved by any single-objective optimizer (such as SQP, pattern search etc.). Clearly, the solution obtained will depend on the values (more precisely, the relative values) of the weights specified. For example, if we are trying to maximize the strength of a machine component and minimize the production cost, and if we specify a higher weight for the cost objective compared to the strength, our solution will be one that favors lower cost over higher strength. The solutions obtained using the weighted sum are always Pareto optimal, but coming up with meaningful combinations of weights can be challenging.<ref> A. Messac, A. Ismail-Yahaya, and C. A. Mattson. The Normalized Normal Constraint Method for Generating the Pareto Frontier. Structural and Multidisciplinary Optimization, 25(2):86–98, 2003. </ref>
* Normal Boundary Intersection (NBI) method <ref> I. Das and J. E. Dennis. Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM Journal on Optimization, 8:631–657, 1998. </ref><ref>[http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970005647_1997005080.pdf Normal-Boundary Intersection: An Alternate Method For Generating Pareto Optimal Points In Multicriteria Optimization Problems]</ref>
* Normal Constraint (NC) method.
* Multiobjective Optimization Evolutionary Algorithms (MOEA).<ref>Deb, K. Multi-Objective Optimization using Evolutionary Algorithms John Wiley & Sons, 2001.</ref><ref>Coello Coello, C. A.; Lamont, G. B. & Van Veldhuizen, D. A. Evolutionary Algorithms for Solving Multi-Objective Problems Springer, 2007.</ref>
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