[1]高岳林,杨钦文,王晓峰,等.新型群体智能优化算法综述[J].郑州大学学报(工学版),2022,43(03):21-30.
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新型群体智能优化算法综述()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年03期
页码:
21-30
栏目:
出版日期:
2022-04-10

文章信息/Info

Title:
Summary of New Group Intelligent Optimization Algorithms
作者:
高岳林杨钦文王晓峰李嘉航宋彦杰
文献标志码:
A
摘要:
智能优化算法主要分为4类:仿自然优化算法、进化算法、仿植物生长算法和群体智能优化算法,其中群体智能优化算法是最为重要的一类算法。智能优化算法与图像处理,故障检测、路径规划、粒子滤波,特征选择、生产调度、入侵检测、支持向量机、无线传感器、神经网络等技术领域交叉融合,应用更加广泛。以蝙蝠算法、果蝇优化算法、鲸鱼优化算法、樽海鞘群体算法和哈里斯鹰优化算法为基础,对群体智能优化算法的模型,特征、改进策略及应用领域等进行了综述,从理论研究、改进策略和应用研究3个方面分析了其面临的发展机遇和未来趋势,给出了算法应用的指导意见。研究表明:群体智能优化算法在众多经典问题上的表现较好,而在多目标优化、多约束优化,动态优化和混合变量优化等领域仍有待扩展不同群体智能优化算法在面对各类具体问题时有效的参数控制仍是未来的研究重点种群协同进化、探索更高效的混合算法和搜索策略是可行的解决途径。
Abstract:

Intelligent optimization algorithms could be divided into four categories: nature-like optimization algorithm, evolutionary algorithm, plant growth simulation algorithm, and swarm intelligence optimization algorithm. The swarm intelligence optimization algorithm was the most important type of algorithm. It played an important role in solving complex engineering problems, and together with image processing, fault detec- tion, path planning, particle filtering, feature selection, production scheduling, intrusion detection, support vector machines, wireless sensors, neural network models, and got more extensive applications in other fields. In recent years, intelligent optimization algorithms such as bat algorithm, fruit fly optimization algorithm, whale optimization algorithm, salp swarm algorithm, and harris hawks optimization algorithm were widely used. Based on these five new swarm intelligence optimization algorithm, the model, characteristics, improve- ment strategies and application fields of the algorithm were reviewed. It analyzed the development opportunities and future trends it faced from theoretical investigations, improvement strategy and application studies, and provided a guidance on algorithm application. Findings showed that swarm intelligence optimization algorithm could perform well on many classic problems, but still should be expanded in the fields of multi-objective optimization, multi-constraint optimization, dynamic optimization, and mixed variable optimization. Effective parameter control of different groups of intelligent optimization algorithm in the face of various specific prob- lems was still the focus of future studies. Co-evolution from populations, exploring more efficient hybrid meth- ods and search strategies could be feasible solutions.


更新日期/Last Update: 2022-05-02