Now showing items 1-4 of 4
Convergence versus diversity in multiobjective optimization
Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental ...
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 ...
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 ...
A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization
Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added ...