.png){kind=icon}
返回
Multi-system genetic algorithm for complex system optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-022-07286-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community.
Keyword:
Evolutionary computation
Multi-system genetic algorithm
Complex system optimization
Multi-objective optimization
Migration
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Bandgap tuning in MoSSe bilayers: synergistic effects of dipole moment and interlayer distanceMoSSe双层中的带隙调谐: 偶极矩和层间距离的协同效应
.png){kind=icon}
PREAI
分享收藏Potential therapeutic effects of GS-441524 and GC376 in cats with feline infectious peritonitisGS-441524和GC376在猫传染性腹膜炎中的潜在治疗作用
OAAI
OAAI分享收藏Distributed evolutionary algorithms and their models: A survey of the state-of-the-art分布式进化算法及其模型: 最新技术综述
OAAI
OAAI分享收藏分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey自然启发优化算法中的多种群技术: 综合调查
PREAI
分享收藏
查看更多内容