Dual-Archive Large-Scale Sparse Optimization Algorithm Based on Dynamic Adaption

被引：0
|
作者：
Gu Q. [1 ,2 ,3 ]
Wang C. [1 ,2 ]
Jiang S. [2 ,3 ]
Chen L. [1 ,2 ,3 ]
机构：
[1] School of Management, Xi'an University of Architecture and Technology, Xi'an
[2] Institute of Mine Systems Engineering, Xi'an University of Architecture and Technology, Xi'an
[3] School of Resources Engineering, Xi'an University of Architecture and Technology, Xi'an
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Dual-Archive; Dynamic Adaptation; Inertial Weight; Large-Scale; Sparse Optimization Algorithm;
D O I：
10.16451/j.cnki.issn1003-6059.202107002
中图分类号：
学科分类号：
摘要：
The traditional large-scale optimization algorithms generate high dimensionality and sparseness problems. A dual-archive large-scale sparse optimization algorithm based on dynamic adaptation is proposed to keep the balance of dimensionality and sparseness in the algorithm and improve the diversity and convergence performance of the algorithm in solving large-scale optimization problems. Firstly, the scores strategy for generating population is changed. By adding adaptive parameter and inertia weight, the dynamics of scores is increased, the diversity of the population is improved, and it is not easy to fall into the local optimum. Secondly, the environment selection strategy of the algorithm is changed by introducing the concept of angle truncation, and the offspring is generated effectively. Meanwhile, a double-archive strategy is introduced to separate the real decision variables from the binary decision variables and thus the running time of the algorithm is reduced. The experimental results on problems of large-scale optimization, sparse optimization and practical application show that the proposed algorithm maintains the original sparsity with steadily improved diversity and convergence and strong competitiveness. © 2021, Science Press. All right reserved.
引用
收藏
页码：592 / 604
页数：12
相关论文
共 29 条
  • [11] TIAN Y, LU C, ZHANG X Y, Et al., Solving Large-Scale Multi-objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks, IEEE Transactions on Cybernetics, 51, 6, pp. 3115-3128, (2021)
  • [12] TIAN Y, ZHANG X Y, WANG C, Et al., An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems, IEEE Transactions on Evolutionary Computation, 24, 2, pp. 380-393, (2020)
  • [13] WANG Y R, ZHANG D M., Butterfly Optimization Algorithm Combining Sine Cosine and Iterative Chaotic Map with Infinite Collapses, Pattern Recognition and Artificial Intelligence, 33, 7, pp. 660-669, (2020)
  • [14] HEGAZY A E, MAKHLOUF M A, EL-TAWEL G S., Improved Salp Swarm Algorithm for Feature Selection, Journal of King Saud University(Computer and Information Sciences), 32, 3, pp. 335-344, (2020)
  • [15] LIU J S, HUO Y, LI Y., Preferred Strategy Based Self-adaptive Ant Lion Optimization Algorithm, Pattern Recognition and Artificial Intelligence, 33, 2, pp. 121-132, (2020)
  • [16] DEB K, PRATAP A, AGARWAL S, Et al., A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)
  • [17] CHUGH T, JIN Y C, MIETTINEN K, Et al., A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 22, 1, pp. 129-142, (2018)
  • [18] SHANG R H, DAI K Y, JIAO L C, Et al., Improved Memetic Algorithm Based on Route Distance Grouping for Multiobjective Large Scale Capacitated Arc Routing Problems, IEEE Transactions on Cybernetics, 46, 4, pp. 1000-1013, (2017)
  • [19] ZILLE H., Large-Scale Multi-objective Optimisation: New Approaches and a Classification of the State-of-the-Art
  • [20] HE C, CHENG R, YAZDANI D., Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization, IEEE Transactions on Systems, Man, and Cybernetics(Systems), (2020)
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