.png){kind=icon}
返回
Sparse Large-Scale Multiobjective Optimization by Identifying Nonzero Decision Variables
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2024.3418346.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sparse large-scale evolutionary multiobjective optimization has garnered substantial interest over the past years due to its significant practical implications. These optimization problems are characterized by a predominance of zero-valued decision variables in the Pareto optimal solutions. Most existing algorithms focus on exploiting the sparsity of solutions by starting with initializing all decision variables with a nonzero value. Opposite to the existing approaches, we propose to initialize all decision variables to zero, then progressively identify and optimize the nonzero ones. The proposed framework consists of two stages. In the first stage of evolutionary optimization, a clustering method is applied at a predefined period of generations to identify nonzero decision variables according to the statistics of each variable's current and historical values. Once a new nonzero decision variable is identified, it is randomly initialized within one of the two intervals, one defined by its lower quartile and lower bound, and the other by its upper quartile and upper bound. In the second stage, the clustering method is also periodically employed to distinguish between zero and nonzero decision variables. Different to the first stage, the zero decision variables will be set to zero straight, and the nonzero decision variables will be mutated at a higher probability. The performance of the proposed framework is empirically examined against state-of-the-art evolutionary algorithms on both sparse and nonsparse benchmarks and real-world problems, demonstrating its superior performance on different classes of problems.
Keyword:
Evolutionary algorithms
large-scale optimization
multiobjective optimization problems (MOPs)
sparse optimization problems
Evolutionary algorithms
large-scale optimization
multiobjective optimization problems (MOPs)
sparse optimization problems
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
.png){kind=icon}
OAAI分享收藏Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in Decision and Objective Spaces决策和目标空间中具有双重聚类的多模态多目标进化优化
PREAI
分享收藏分享收藏An Enhanced Competitive Swarm Optimizer With Strongly Convex Sparse Operator for Large-Scale Multiobjective Optimization用于大规模多目标优化的具有强凸稀疏算子的增强型竞争群优化器
PREAI
分享收藏A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization基于聚类的稀疏截断竞争粒子群算法的多目标优化
PREAI
分享收藏Graph-Based Deep Decomposition for Overlapping Large-Scale Optimization Problems基于图的深度分解求解重叠大规模优化问题
OAAI
OAAI分享收藏分享收藏
查看更多内容