For the selected techniques, performances over a set of benchmarks will be presented, as well as results over relevant electronic engineering problems. Keywords: multi-objective optimization; evolutionary algorithms; genetic algorithmsdoi:10.1002/047134608X.W8226Elson AgastraGiuseppe PelosiStefano SelleriRuggero TaddeiJohn Wiley & Sons, Inc.
Evolutionary algorithms like genetic algorithms, particle swarm optimization and simulated annealing fall under this category, as do Multi-objective Genetic Algorithms such as NSGA-II, SPEA2, MOEA/D and NSGA-II19. Interactive techniques: these strategies necessitate human engagement throughout the ...
2. Multi-objective optimization techniques There have been several attempts to classify the multi-objective optimization techniques currently in use. First of all, it is quite important to distinguish the two stages in which the solution of a multi-objective optimization problem can be divided: the...
For this purpose, multiobjective optimization techniques may be used. In a multiobjective optimization problem,46–49 multiple objective functions F1(u), F2(u), …, Fn(u) must be simultaneously maximized or minimized (or a combination of both) with respect to vector u. In the variable ...
Multiobjective optimization decisions require parameters for this kind of problem-solving activity. Optimization of complex systems is considered a science, and experts differ on the best techniques for optimization. Initially, listing all objectives and their potential variability under differing situations ...
Multiobjective optimization: interactive and evolutionary approaches, Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen Multi-objective optimization: techniques and applications in chemical Engineering, Gade Pandu Rangaiah See also Multidisciplinary design optimization ...
2.3 Techniques to Solve Multi-objective Optimization Problems 17 ? Non-convex parts of the Pareto set cannot be reached by minimizing convex combinations of the objective functions. An example can be made showing a geometrical interpretation of the weighted-sum method in two dimensions, i.e., ...
Ma¨kela¨, "On scalarizing functions in multiobjective optimization", OR Spectrum 24 (2002) 193-213.Miettinen, K.; Makela, M.M. On scalarizing functions in multiobjective optimization. OR Spectr. 2002, 24, 193-213. [CrossRef]Miettinen, K., M¨akel¨a, M.M.: On scalarizing ...
IEEEBranke, J., Deb, K., Miettinen, K., Słowiński, R.: Multiobjective Optimization: Interactive and Evolutionary Approaches. Springer, Berlin (2008)Branke J, Deb K, Miettinen K, Slowinski R (2008) Multiobjective optimisation: interactive and evolutionary approaches. Springer, Berlin...
We refer to these two methods as ‘Monte Carlo Multi-Objective Optimization Algorithms’ or as MCMO algorithms. In multi-objective optimization, we have a set of functions{f1(x),f2(x),…,fm(x)},each member of which specifies a material property as a function of the same multi-featured ...