In the past few decades, to solve the multi-objective optimization problems, many multi-
objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a …

In many-objective optimization, both convergence and diversity are equally important.
However, in high-dimensional spaces, traditional decomposition-based many-objective …

Pareto dominance-based many-objective evolutionary algorithms (MaOEAs) face a
significant challenge from many-objective problems (MaOPs). The selection pressure …

When dealing with regular, simple Pareto fronts (PFs), the decomposition-based multi-
objective optimization algorithm (MOEA/D) performs well by presetting a set of uniformly …

The multi-objective directed acyclic graph scheduling problem (MDAGSP) is prevalent in
cloud scheduling systems, involving the selection, assignment, and execution of multiple …

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes
a multi-objective problem into a series of single-objective subproblems for collaborative …

Many-objective optimization problems involve numerous objective functions, leading to
larger and more intricate Pareto fronts. Conventional evolutionary algorithms struggle to …

随着目标维度的上升, 高维多目标优化问题的帕累托前沿越来越复杂, 传统的基于分解的高维多
目标进化算法难以挑选出多样性和收敛性良好的种群. 针对以上问题提出了一种面向高维多目标 …

Evolutionary algorithms with limited fitness evaluations may find it difficult to solve
optimization issues requiring significant computational resources. The performance of the …