.png){kind=icon}
返回
An improved differential evolution with adaptive population allocation and mutation selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.125130.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In order to improve the performance of differential evolution (DE) for different optimization problems, an improved DE with adaptive population allocation and mutation selection (iDE-APAMS) is proposed. First, different mutation strategies are selected to constitute the exploration strategy pool and the exploitation strategy pool. The exploration strategy pool is mainly used for global search to increase the population diversity; the exploitation strategy pool is primarily used for local search to accelerate the convergence and improve the solution accuracy. Second, different mutation strategies obtain population resources dynamically through cooperation and competition. Mutation strategies in the same strategy pool first compete for population resources with mutation strategies in the other strategy pool through cooperation, and in this way the population is allocated between the two strategy pools. Then within each strategy pool, different mutation strategies compete with each other to obtain corresponding population resources. Third, mutation scale factor and crossover rate are adaptively adjusted based on population diversity and fitness improvement. Finally, the Levy random walk is applied to individuals with better fitness at the later stage of the iteration to avoid falling into local optima and further improve the accuracy of the solution. The iDE-APAMS is compared with 4 classical DE variants and 11 state-of-the-art algorithms on the CEC2013, CEC2014, and CEC2017 benchmark test suites respectively, and it is also tested on 22 real-world optimization problems in CEC2011. The results show that iDE-APAMS can improve the convergence of the algorithm and the stability of the solution, and it is statistically significantly better than the comparison algorithms.
Keyword:
Diversity
Exploration and exploitation
Fitness improvement
Mutation selection
Population allocation
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution使用空间邻域进行参数自适应: 基于成功历史的改进差分进化
OAAI
OAAI分享收藏HARD-DE: Hierarchical ARchive Based Mutation Strategy With Depth Information of Evolution for the Enhancement of Differential Evolution on Numerical Optimization.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization
PREAI
分享收藏Differential evolution with orthogonal array -based initialization and a novel selection strategy
PREAI
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Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae in Hospitalized Neonatal Foals: Prevalence, Risk Factors for Shedding and Association with InfectionAnimalsOAAI
AnimalsIF0
OAAI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