.png){kind=icon}
返回
A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3538537.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.
Keyword:
Heuristic algorithms
Optimization
Convergence
Pareto optimization
Classification algorithms
Prediction algorithms
Diversity reception
Vectors
Optical fibers
Machine learning algorithms
Dynamic multi-objective optimization
decision variable classification
irregular environment
neural network
prediction
special point
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons进化动态多目标优化: 基准和算法比较
PREAI
分享收藏A particle swarm algorithm based on the dual search strategy for dynamic multi-objective optimization基于双搜索策略的动态多目标优化粒子群算法
PREAI
分享收藏Elevation of amniotic fluid interleukin 6 (IL-6), IL-8 and granulocyte colony stimulating factor (G-CSF) in term and preterm parturition.png)
Cytokine
CytokineIF0
PREAI
分享收藏A directed search strategy for evolutionary dynamic multiobjective optimizationSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏A framework based on generational and environmental response strategies for dynamic multi-objective optimization基于世代和环境响应策略的动态多目标优化框架
OAAI
OAAI分享收藏An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimizationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容