Chen Jiangtao, Wang Zijia, Kou Zheng
School Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China.
Biomimetics (Basel). 2024 Nov 1;9(11):664. doi: 10.3390/biomimetics9110664.
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.
进化多任务优化（EMTO）是目前最热门的研究课题之一，旨在利用任务之间的相关性同时对其进行优化。尽管已经提出了许多基于传统差分进化（DE）和遗传算法（GA）的进化多任务算法（EMTA），但基于粒子群优化（PSO）的EMTA相对较少。与DE和GA相比，PSO具有更快的收敛速度，尤其是在进化过程的后期阶段。因此，本文提出了一种基于多任务层次的学习群体优化器（MTLLSO）。在MTLLSO中，维护多个群体，每个群体分别使用LLSO对应于一个任务的优化，利用适应度更好的高级个体来指导适应度较差的低级个体的进化。当发生信息传递时，来自源群体的高级个体被用于指导目标群体中低级个体的进化，以促进知识传递的有效性。通过这种方式，MTLLSO可以在自我进化和知识传递之间获得令人满意的平衡。我们已经在CEC2017基准测试中展示了MTLLSO的有效性，在大多数问题上，MTLLSO显著优于其他比较算法。
