Web Reference: Learn about the mathematical optimization problems involving more than one objective function to be optimized simultaneously. Find examples, applications, methods and solution philosophies for multi-objective optimization problems in various fields. Learn the definition, formulation and methods of multi-objective optimization problems (MOOP) with multiple objectives to be minimized or maximized. Compare classic and evolutionary approaches, such as weighted sum, ε-constraint, weighted metric and multi-objective genetic algorithms. The basic loop of NSGA-II (Deb et al. 2002) is given by Algorithm 1. Firstly, a population of points is initialized. Then the following generational loop is repeated. This loop consists of two parts. In the first, the population undergoes a variation. In the second part, a selection takes place which results in the new generation-population. The ge...
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