Multitask Level-Based Learning Swarm Optimizer
Source abstract
Abstract
Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditional differential evolution (DE) and the genetic algorithm (GA) have been proposed, there are relatively few EMTAs based on particle swarm optimization (PSO). Compared with DE and GA, PSO has a faster convergence speed, especially during the later state of the evolutionary process. Therefore, this paper proposes a multitask level-based learning swarm optimizer (MTLLSO). In MTLLSO, multiple populations are maintained and each population corresponds to the optimization of one task separately using LLSO, leveraging high-level individuals with better fitness to guide the evolution of low-level individuals with worse fitness. When information transfer occurs, high-level individuals from a source population are used to guide the evolution of low-level individuals in the target population to facilitate the effectiveness of knowledge transfer. In this way, MTLLSO can obtain the satisfying balance between self-evolution and knowledge transfer. We have illustrated the effectiveness of MTLLSO on the CEC2017 benchmark, where MTLLSO significantly outperformed other compared algorithms in most problems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
Funding
National Natural Science Foundation of China (NSFC) · Guangdong Natural Science Foundation · Guangzhou Science and Technology Planning Project
References (38)
Show source-provided references
1. Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN’95-International Conference on Neural Networks, Perth, WA, Australia.
2. Adaptive Particle Swarm Optimization10.1109/TSMCB.2009.2015956 ↗
3. Hubálovský, Š., Hubálovská, M., and Matoušová, I. (2023). A New Hybrid Particle Swarm Optimization–Teaching–Learning-Based Optimization for Solving Optimization Problems. Biomimetics, 9.10.3390/biomimetics9010008 ↗
4. Melanie, M. (1999). An Introduction to Genetic Algorithms, The MIT Press.
5. Learning-Aided Evolution for Optimization10.1109/TEVC.2022.3232776 ↗
6. Matrix-Based Evolutionary Computation10.1109/TETCI.2020.3047410 ↗
7. Ant Colony Optimization Theory: A Survey10.1016/j.tcs.2005.05.020 ↗
8. Ant Colony Optimization for the Control of Pollutant Spreading on Social Networks10.1109/TCYB.2019.2922266 ↗
9. Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces10.1023/A:1008202821328 ↗
10. Adaptive Distributed Differential Evolution10.1109/TCYB.2019.2944873 ↗
11. Multiobjective Differential Evolution Algorithm Balancing Multiple Stakeholders for Low-Carbon Order Scheduling in E-Waste Recycling10.1109/TEVC.2023.3237336 ↗
12. Jiang, J., Wu, J., Luo, J., Yang, X., and Huang, Z. (2024). MOBCA: Multi-Objective Besiege and Conquer Algorithm. Biomimetics, 9.10.3390/biomimetics9060316 ↗
13. A Many-Objective Particle Swarm Optimizer with Leaders Selected from Historical Solutions by Using Scalar Projections10.1109/TCYB.2018.2884083 ↗
14. Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems10.1109/TSMCB.2012.2209115 ↗
15. Dual-Strategy Differential Evolution with Affinity Propagation Clustering for Multimodal Optimization Problems10.1109/TEVC.2017.2769108 ↗
16. A Scatter Learning Particle Swarm Optimization Algorithm for Multimodal Problems10.1109/TCYB.2013.2279802 ↗
17. Fitness and Distance Based Local Search with Adaptive Differential Evolution for Multimodal Optimization Problems10.1109/TETCI.2023.3234575 ↗
18. A Distributed Swarm Optimizer with Adaptive Communication for Large-Scale Optimization10.1109/TCYB.2019.2904543 ↗
19. Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization10.1109/TEVC.2022.3185665 ↗
20. SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems10.1109/TEVC.2021.3051608 ↗
21. Hong, J., Zhan, Z.-H., He, L., Xu, Z., and Zhang, J. (2024). Protein Structure Prediction Using a New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach. IEEE Trans. Evol. Computat., 1.10.1109/TEVC.2024.3365814 ↗
22. An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing10.1109/TEVC.2016.2623803 ↗
23. Dynamic Group Learning Distributed Particle Swarm Optimization for Large-Scale Optimization and Its Application in Cloud Workflow Scheduling10.1109/TCYB.2019.2933499 ↗
24. Surrogate-Assisted Hybrid-Model Estimation of Distribution Algorithm for Mixed-Variable Hyperparameters Optimization in Convolutional Neural Networks10.1109/TNNLS.2021.3106399 ↗
25. Multifactorial Evolution: Toward Evolutionary Multitasking10.1109/TEVC.2015.2458037 ↗
26. Deb, K. (2011). Multi-Objective Optimization Using Evolutionary Algorithms: An Introduction. Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing, Springer.10.1007/978-0-85729-652-8_1 ↗
27. Ehrgott, M. (2005). Multicriteria Optimization, Springer. [2nd ed.].
28. Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-II10.1109/TEVC.2019.2906927 ↗
29. Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization10.1109/TFUZZ.2020.2968863 ↗
30. Feng, L., Zhou, W., Zhou, L., Jiang, S.W., Zhong, J.H., Da, B.S., Zhu, Z.X., and Wang, Y. (2017, January 5–8). An Empirical Study of Multifactorial PSO and Multifactorial DE. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain.10.1109/CEC.2017.7969407 ↗
31. Domain Adaptation Multitask Optimization10.1109/TCYB.2022.3222101 ↗
32. Block-Level Knowledge Transfer for Evolutionary Multitask Optimization10.1109/TCYB.2023.3273625 ↗
33. A Level-Based Learning Swarm Optimizer for Large-Scale Optimization10.1109/TEVC.2017.2743016 ↗
34. Da, B., Ong, Y.-S., Feng, L., Qin, A.K., Gupta, A., Zhu, Z., Ting, C.-K., Tang, K., and Yao, X. (2017). Evolutionary Multitasking for Single-Objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results. arXiv.
35. Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation10.1109/TCYB.2020.2974100 ↗
36. Evolutionary Multitasking via Explicit Autoencoding10.1109/TCYB.2018.2845361 ↗
37. A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization10.1109/TEVC.2021.3131236 ↗
38. Evolutionary Multitasking via Reinforcement Learning10.1109/TETCI.2023.3281876 ↗