Dual-Archive Large-Scale Sparse Optimization Algorithm Based on Dynamic Adaption
GU Qinghua1,2,3, WANG Chuhao1,2, JIANG Song2,3, CHEN Lu1,2,3
1. School of Management, Xi′an University of Architecture and Technology, Xi′an 710055; 2. Institute of Mine Systems Engineering, Xi′an University of Architecture and Technology, Xi′an 710055; 3. School of Resources Engineering, Xi′an University of Architecture and Technology, Xi′an 710055
Abstract 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.
Fund:National Natural Science Foundation of China(No.51974223), Outstanding Youth Project of Shaanxi Natural Science Foundation(No. 2020JC-44), Joint Fund Project of Shaanxi Natural Science Basic Research Foundation(No. 2019JLP-16)
Corresponding Authors:
GU Qinghua, Ph.D., professor. His research interests include mining system engineering.
About author:: WANG Chuhao, master student. His research interests include multi-objective system optimization.JIANG Song, Ph. D., associate professor. His research interests include optimization mo-deling of intelligent mine.CHEN Lu, Ph.D. candidate. His research interests include multi-objective system optimization.
[1] TIAN Y, CHENG R, ZHANG X Y, et al. A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutio-nary Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 2019, 23(2): 331-345. [2] SAID L B, BECHIKH S, GHEDIRA K. The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making. IEEE Transactions on Evolutionary Computation, 2010, 14(5): 801-818. [3] WANG H D, JIAO L C, YAO X. Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 2015, 19(4): 524-541. [4] ZHANG X Y, TIAN Y, CHENG R, et al. A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 2018, 22(1): 97-112. [5] 顾清华,莫明慧,卢才武,等.求解约束高维多目标问题的分解约束支配NSGA-II优化算法.控制与决策, 2020, 35(10): 2466-2474. (GU Q H, MO M H, LU C W, et al. Decomposition-Based Constrained Dominance Principle NSGA-II for Constrained Many-Objective Optimization Problems. Control and Decision, 2020, 35(10): 2466-2474. [6] 顾清华,李学现,卢才武,等.求解高维复杂函数的遗传-灰狼混合算法.控制与决策, 2020, 35(5): 1191-1198. (GU Q H, LI X X, LU C W, et al. Hybrid Genetic Grey Wolf Algorithm for High Dimensional Complex Function Optimization. Control and Decision, 2020, 35(5): 1191-1198.) [7] ZILLE H, ISHIBUCHI H, MOSTAGHIM S, et al. A Framework for Large-Scale Multiobjective Optimization Based on Problem Transfor-mation. IEEE Transactions on Evolutionary Computation, 2018, 22(2): 260-275. [8] TIAN Y, ZHENG X T, ZHANG X Y, et al. Efficient Large-Scale Multi-objective Optimization Based on a Competitive Swarm Optimizer. IEEE Transactions on Cybernetics, 2020, 50(8): 3696-3708. [9] WANG L D, LIU Z P, FENG Y T, et al. A Parameter Estimation Method for Time-Frequency-Overlapped Frequency Hopping Signals Based on Sparse Linear Regression and Quadratic Envelope Optimi-zation. International Journal of Communication Systems, 2020, 33(12): 575-584. [10] 肖 文,胡 娟.稀疏数据频繁项集挖掘算法研究综述.计算机工程与科学, 2019, 41(5): 780-787. (XIAO W, HU J. A Survey of Frequent Itemset Mining Algorithms for Sparse Datase. Computer Engineering and Science, 2019, 41(5): 780-787.) [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, 2021, 51(6): 3115-3128. [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, 2020, 24(2): 380-393. [13] 王依柔,张达敏.融合正弦余弦和无限折叠迭代混沌映射的蝴蝶优化算法.模式识别与人工智能, 2020, 33(7): 660-669. (WANG Y R, ZHANG D M. Butterfly Optimization Algorithm Combining Sine Cosine and Iterative Chaotic Map with Infinite Co-llapses. Pattern Recognition and Artificial Intelligence, 2020, 33(7): 660-669.) [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), 2020, 32(3): 335-344. [15] 刘景森,霍 宇,李 煜.优选策略的自适应蚁狮优化算法.模式识别与人工智能, 2020, 33(2): 121-132. (LIU J S, HUO Y, LI Y. Preferred Strategy Based Self-adaptive Ant Lion Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2020, 33(2): 121-132.) [16] DEB K, PRATAP A, AGARWAL S, et al. A Fast and Elitist Mu-ltiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. [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, 2018, 22(1): 129-142. [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, 2017, 46(4): 1000-1013. [19] ZILLE H. Large-Scale Multi-objective Optimisation: New Approaches and a Classification of the State-of-the-Art[C/OL]. [2021-02-28]. https://opendata.uni-halle.de/bitstream/1981185920/32220/1/Zille_Heiner_Dissertation_2019.pdf. [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. DOI: 10.1109/TSMC.2020.3003926. [21] LIU Y P, YEN G G, GONG D W. A Multi-modal Multi-objective Evolutionary Algorithm Using Two-Archive and Recombination Strategies. IEEE Transactions on Evolutionary Computation, 2019, 23(4): 660-674. [22] TIAN Y, LU C, ZHANG X Y, et al. A Pattern Mining Based Evolutionary Algorithm for Large-Scale Sparse Multi-objective Optimization Problems. IEEE Transactions on Cybernetics, 2020. DOI: 10.1109/TCYB.2020.3041325. [23] CHENG R, JIN Y C, OLHOFER M, et al. Test Problems for Large-Scale Multiobjective and Many-Objective Optimization. IEEE Tran-sactions on Cybernetics, 2017, 47(12): 4108-4121. [24] HUBAND S, HINGSTON P, BARONE L, et al. A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Transactions on Evolutionary Computation, 2006, 10(5): 477-506. [25] TIAN Y, CHENG R, ZHANG X Y, et al. PlatEMO: A MATLAB Platform for Evolutionary Multi-objective Optimization. IEEE Computational Intelligence Magazine, 2017, 12(4): 73-87. [26] VILLALOBOS C A K, COELLO COELLO C A. A New Multi-objective Evolutionary Algorithm Based on a Performance Assessment Indicator//Proc of the 14th Annual Conference on Genetic and Evolutionary Computation. New York, USA: ACM, 2012: 505-512. [27] TIAN Y, CHENG R, ZHANG X Y, et al. Diversity Assessment of Multi-objective Evolutionary Algorithms: Performance Metric and Benchmark Problems. IEEE Computational Intelligence Magazine, 2019, 14(3): 61-74. [28] LIAO X T, LI Q, YANG X J, et al. Multiobjective Optimization for Crash Safety Design of Vehicles Using Stepwise Regression Mo-del. Structural and Multidisciplinary Optimization, 2008, 35(6): 561-569. [29] 顾清华,周煜丰,李学现,等.基于径向空间划分的昂贵多目标进化算法[J/OL]. [2021-02-28]. https://doi.org/10.16383/j.aas.c200791. (GU Q H, ZHOU Y F, LI X X, et al. Expensive Many-Objective Evolutionary Algorithm Based on Radial Space Division[J/OL]. [2021-02-28]. https://doi.org/10.16383/j.aas.c200791.)
2021, Vol. 34 
