Zheng Xiaolong, Zhou Deyun, Li Na, Wu Tao, Lei Yu, Shi Jiao
School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an 710072, China.
Sensors (Basel). 2021 Nov 11;21(22):7499. doi: 10.3390/s21227499.
Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.
基于群体的搜索技术因其良好的性能已得到发展并应用于广泛的领域，例如无人机（UAV）路径规划问题的优化。然而，对于一个优化问题寻找最优解通常成本很高。例如，无人机问题是一个具有许多约束条件的大规模优化问题，这使得难以获得精确解。特别是，当多个无人机问题同时等待优化时会很耗时。进化多任务优化（EMTO）研究利用进化计算技术基于群体的特性同时优化多个优化问题，目的是进一步提高解决所有这些问题的整体性能。EMTO在更高效地解决实际问题方面具有巨大潜力。因此，在本文中，我们使用经典的粒子群优化（PSO）算法开发了一种新颖的EMTO算法，其中所开发的知识转移策略在群体速度更新期间通过合成从一组选定的组件任务转移来的知识，实现任务间的知识转移。伴随着所提出算法的两个版本，开发了两种知识转移策略。在所提出的算法与多因素PSO算法、SREMTO算法、流行的多因素进化算法以及经典PSO算法在九个流行的单目标多任务优化（MTO）问题和六个五任务MTO问题上进行了比较，这证明了其优越性。
