基于自适应迁移知识来源的约束多因子进化算法
A Constrained Multifactorial Evolutionary Algorithm Based on Adaptive Transferred Knowledge Sources
摘要: 在约束多任务优化中，如何在任务之间迁移知识是一个复杂的问题。现有的方法是从源任务的种群中随机选择个体进行迁移，忽略了源任务中一些目标函数值好的个体对目标任务的积极影响。本文针对约束多任务优化问题，提出基于自适应迁移知识来源的约束多因子进化算法。首先，利用一个归档集来存储每个任务中目标函数值最好的个体，用于产生高质量子代。然后，提出一个自适应迁移知识来源策略，通过一个自适应概率来确定被迁移的个体是来自于种群还是归档集。实验结果表明，与其他现有的约束多任务进化算法相比，所提算法可以产生更好的或至少相当的性能。
Abstract: In constrained multitask optimization, transferring knowledge between tasks is a complex problem. Existing methods randomly select individuals from the source task’s population for transfer, disregarding the positive impact of individuals with good objective function value on the target task. This paper proposes a constrained multifactorial evolutionary algorithm based on adaptive transferred knowledge sources. Firstly, an archive is used to store individuals with the best objective function value in each task to generate high-quality offspring. Then, an adaptive transfer knowledge source strategy determines whether the transferred individuals are from the population or the archive through adaptive probability. The experimental results demonstrate that the proposed algorithm can achieve better or at least comparable performance.
文章引用：崔春玲. 基于自适应迁移知识来源的约束多因子进化算法[J]. 运筹与模糊学, 2024, 14(2): 171-180. https://doi.org/10.12677/orf.2024.142123

参考文献

[1] Gupta, A., Ong, Y. and Feng, L. (2016) Multifactorial Evolution: Toward Evolutionary Multitasking. IEEE Transactions on Evolutionary Computation, 20, 343-357. [Google Scholar] [CrossRef] 
[2] Feng, L., Huang, Y., Zhou, L., Zhong, J., Gupta, A., Tang, K. and Tan, K.C. (2020) Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem. IEEE Transactions on Cybernetics, 51, 3143-3156. [Google Scholar] [CrossRef] 
[3] Wu, D. and Tan, X. (2018) Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization. IEEE Transactions on Fuzzy Systems, 28, 1050-1061. [Google Scholar] [CrossRef] 
[4] Wang, Z. and Wang, X. (2019) Multiobjective Multifactorial Operation Optimization for Continuous Annealing Production Process. Industrial & Engineering Chemistry Research, 58, 19166-19178. [Google Scholar] [CrossRef] 
[5] Wu, Y., Cai, Y. and Fang, C. (2023) Evolutionary Multitasking for Bidirectional Adaptive Codec: A Case Study on Vehicle Routing Problem with Time Windows. Applied Soft Computing, 145, Article ID: 110605. [Google Scholar] [CrossRef] 
[6] Cheng, B., Fu, H., Li, T., Zhang, H., Huang, J., Peng, Y., Chen, H. and Fan, C. (2023) Evolutionary Computation-Based Multitask Learning Network for Railway Passenger Comfort Evaluation from EEG Signals. Applied Soft Computing, 136, Article ID: 110079. [Google Scholar] [CrossRef] 
[7] Li, Y., Gong, W. and Li, S. (2022) Multitasking Optimization via an Adaptive Solver Multitasking Evolutionary Framework. Information Sciences, 630, 688-712. [Google Scholar] [CrossRef] 
[8] Li, Y., Gong, W. and Li, S. (2022) Evolutionary Constrained Multi-Task Optimization: Benchmark Problems and Preliminary Results. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 9-13 July 2022, Boston, MA, 443-446. [Google Scholar] [CrossRef] 
[9] Deb, K. (2000) An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering, 186, 311-338. [Google Scholar] [CrossRef] 
[10] Xing, C., Gong, W. and Li, S. (2023) Adaptive Archive-Based Multifactorial Evolutionary Algorithm for Constrained Multitasking Optimization. Applied Soft Computing, 143, Article ID: 110385. [Google Scholar] [CrossRef] 
[11] Xue, X., Zhang, K., Tan, K.C., Feng, L., Wang, J., Chen, G., Zhao, X., Zhang, L. and Yao, J. (2020) Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems. IEEE Transactions on Cybernetics, 52, 6217-6231. [Google Scholar] [CrossRef] 
[12] Feng, L., Zhou, W., Zhou, L., Jiang, S., Zhong, J., Da, B., Zhu, Z. and Wang, Y. (2017) An Empirical Study of Multifactorial PSO and Multifactorial DE. Proceedings of 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain, 5-8 June 2017, 921-928. [Google Scholar] [CrossRef] 
[13] Bali, K.K., Ong, Y., Gupta, A. and Tan, P.S. (2020) Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-II. IEEE Transactions on Evolutionary Computation, 24, 69-83. [Google Scholar] [CrossRef] 
[14] Li, G., Lin, Q. and Gao, W. (2020) Multifactorial Optimization via Explicit Multipopulation Evolutionary Framework. Information Sciences, 512, 1555-1570. [Google Scholar] [CrossRef] 
[15] Wang, B., Li, H., Zhang, Q. and Wang, Y. (2018) Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 574-587. [Google Scholar] [CrossRef] 

为你推荐