Content deleted Content added
Line 33:
* 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><ref>Das, S.; Panigrahi, B. K. Multi-objective Evolutionary Algorithms, Encyclopedia of Artificial Intelligence, (Eds. J. R. Rabuñal, J. Dorado & A. Pazos), Idea Group Publishing, 3:1145 – 1151, 2008.</ref>
[[Evolutionary algorithms]] are very popular approaches in multiobjective optimization. Nowadays, most evolutionary optimizers apply Pareto-based ranking schemes. Genetic algorithms such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Approach 2 (SPEA-2) have become standard approaches, although some schemes based on particle swarm optimization and simulated annealing are significant.
* PGEN (Pareto surface generation for convex multiobjective instances)<ref> D. Craft, T. Halabi, H. Shih, and T. Bortfeld. Approximating convex Pareto surfaces in multiobjective radiotherapy planning. Medical Physics, 33(9):3399–3407, 2006.</ref>
* [[IOSO]] (Indirect Optimization on the basis of Self-Organization)
| |||