Now showing items 1-10 of 18
Pareto or Non-Pareto: Bi-Criterion Evolution in Multi-Objective Optimization
It is known that Pareto dominance has its own weaknesses as the selection criterion in evolutionary multi-objective optimization. Algorithms based on Pareto dominance can suffer from slow convergence to the optimal front, ...
ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization
(MIT Press, 2014-05)
The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since ...
Multiobjective optimization of the production process for ground granulated blast furnace slags
The production process of ground granulated blast furnace slag (GGBS) aims to produce products of the best grade and the highest yields. However, grade and yields are two competing objectives which can not be optimized at ...
Diversity comparison of Pareto front approximations in many-objective optimization
(IEEE Press, 2014-04-03)
Diversity assessment of Pareto front approximations is an important issue in the stochastic multiobjective optimization community. Most of the diversity indicators in the literature were designed to work for any number of ...
IPESA-II: Improved Pareto envelope-based selection algorithm II
Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization
(University of Birmingham, U.K., 2017-01)
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary ...
Multi-line distance minimization: A visualized many-objective test problem suite
Studying the search behavior of evolutionary manyobjective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing ...
A grid-based evolutionary algorithm for many-objective optimization
Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties in their ...
A Performance Comparison Indicator for Pareto Front Approximations in Many-Objective Optimization
(ACM Press, 2015-07)
Increasing interest in simultaneously optimizing many objectives (typically more than three objectives) of problems leads to the emergence of various many-objective algorithms in the evolutionary multi-objective optimization ...