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| 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 |
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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.
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Received: 16 March 2021
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| 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) |
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Corresponding Authors:
GU Qinghua, Ph.D., professor. His research interests include mining system engineering.
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| 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. |
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2021, Vol. 34 
