Now showing items 1-6 of 6
A many-objective evolutionary algorithm based on rotated grid
Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which ...
A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation
Evolutionary algorithms (EAs) have shown to be efficient in dealing with many-objective optimization problems (MaOPs) due to their ability to obtain a set of compromising solutions which not only converge toward the Pareto ...
A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization
Among many-objective optimization problems (MaOPs), the proportion of nondominated solutions is too large to distinguish among different solutions, which is a great obstacle in the process of solving MaOPs. Thus, this paper ...
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 ...
An adaptation reference-point-based multiobjective evolutionary algorithm
It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization ...
Bi-goal evolution for many-objective optimization problems
This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed ...