It is possible to conclude that the parallelization has a positive effect on the convergence and diversity of the optimization process for problems with many objectives. However, there is no single strategy that is the best results for all classes of problems. In terms of scalability, for higher...
Nevertheless, balancing proximity and diversity using one single criterion is not an easy task [76], [38], [69], [68], especially for a many-objective optimization problem in which the conflict between the objectives is generally more serious than that in an MOP with two or three objectives...
Kang, Z.et al.(2007). A New Evolutionary Decision Theory for Many-Objective Optimization Problems. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/1...
this class of problems, since a DM will eventually need to select a single solution from the huge number of Pareto-optimal ones—in other words, solving the problem of finding Pareto-optimal points does not necessarily mean that one has solved the practical many-objective optimization problem. ...
When we solve many-objective optimization problems (MaOPs) by using multi-objective evolutionary algorithms (MOEAs), genetic diversity of solutions in the population significantly increases in order to explore the true Pareto optimal solutions widely distributed in variable space. In MOEAs, if solutions...
Evolutionary multicriteria optimization has traditionally concentrated on problems comprising 2 or 3 objectives. While engineering design problems can often be conveniently formulated as multiobjective optimization problems, these often comprise a relati
A key question when tackling such a set problem is how to define the optimization criterion. Many multiobjective evo- lutionary algorithms (MOEAs) implement a combination of Pareto dominance on sets and a diversity measure based on Euclidean distance in the objective space, e.g., NSGA-II ...
Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of ...
The selection mechanism of our approach is based on the transformation of a multi-objective optimization problem into a linear assignment problem, which is solved by the Kuhn–Munkres’ (Hungarian) algorithm. Our proposed approach is compared with respect to three state-of-the-art MOEAs, designed...
Consider a many-objective optimization problem with k objectives to be maximized with respect to continuous and/or integer variables. Let f ∈ Rk denote the column vector of objectives. The underlying value function which the decision maker (DM) aims to maximize is u(f). At the outset ...