.png){kind=icon}
返回
Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution
.png){kind=icon}
OA
AI
DOI：10.1109/JAS.2023.123018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.
Keyword:
Control parameters
differential Evolution
metaheuristic algorithms
mutation operator
期刊
I
IF:
19.2
论文数:
1.4K
被引数:
1.1W
机构
引用论文
Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion踝关节背屈良好和不良的人在执行垂直跳跃时的运动学和动力学差异
.png){kind=icon}
PREAI
分享收藏分享收藏
Differential evolution algorithm with fitness and diversity ranking-based mutation operator基于适应度和多样性排序的变异算子差分进化算法
PREAI
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法OAAI
OAAI分享收藏
查看更多内容