Many new bio-inspired algorithms are recently being proposed, artificial tree (AT) algorithm, inspired by the growth of trees and the update behavior of branches, is one of them. There are also some improved AT algorithms being proposed to improve their calculation accuracy. However, the main challenges of AT algorithms lie in the insufficiencies in the design of update operators as well as the position interaction between branches and the capture of key information and the performance of AT algorithms needs to be enhanced. This work proposes an improved AT algorithm with two populations (IATTP). In IATTP, the update strategies of branches are redesigned, and a variety of efficient update operators are designed and applied. The branch population is changed from one to two, and the competition mechanism between populations is proposed. Through the migration of branches between populations, the scale of population with better efficiency is expanded and the size of population with lower efficiency is reduced, thus a reasonable interaction between populations and branches is realized. With above strategies, the efficiency and accuracy of IATTP are significantly improved. The results of IATTP are proved to be advantageous when the performance of IATTP is compared with AT algorithm, improved artificial tree (IAT) algorithm and feedback artificial tree (FAT) algorithm through typical test problems. Meanwhile, the results of IATTP in current state are also preferable when IATTP is compared with other improved algorithms in high dimensional problems. The experimental results prove that IATTP is competitive in solving optimization problems.

最近提出了许多新的仿生算法，受树木生长和树枝更新行为启发的人工树（AT）算法就是其中之一。还有一些改进的AT算法被提出来提高它们的计算精度。然而，AT算法的主要挑战在于更新算子的设计以及分支之间的位置交互和关键信息的捕获等方面的不足，AT算法的性能有待提高。这项工作提出了一种改进的具有两个种群的 AT 算法（IATTP）。在IATTP中，重新设计了分支的更新策略，设计并应用了多种高效的更新算子。分支种群由1个变为2个，提出种群间的竞争机制。通过种群间分支的迁移，扩大了效率较好的种群规模，缩小了效率较低的种群规模，从而实现了种群与分支之间的合理互动。通过以上策略，IATTP的效率和准确率都得到了显着提高。通过典型的测试问题，将IATTP的性能与AT算法、改进人工树（IAT）算法和反馈人工树（FAT）算法进行比较，证明了IATTP的结果是有利的。同时，当IATTP在高维问题中与其他改进算法进行比较时，IATTP在当前状态下的结果也更可取。实验结果证明 IATTP 在解决优化问题方面具有竞争力。扩大效率较好的种群规模，缩小效率较低的种群规模，实现种群与分支的合理互动。通过以上策略，IATTP的效率和准确率都得到了显着提高。通过典型的测试问题，将IATTP的性能与AT算法、改进人工树（IAT）算法和反馈人工树（FAT）算法进行比较，证明了IATTP的结果是有利的。同时，当IATTP在高维问题中与其他改进算法进行比较时，IATTP在当前状态下的结果也更可取。实验结果证明 IATTP 在解决优化问题方面具有竞争力。扩大效率较好的种群规模，缩小效率较低的种群规模，实现种群与分支的合理互动。通过以上策略，IATTP的效率和准确率都得到了显着提高。通过典型的测试问题，将IATTP的性能与AT算法、改进人工树（IAT）算法和反馈人工树（FAT）算法进行比较，证明了IATTP的结果是有利的。同时，当IATTP在高维问题中与其他改进算法进行比较时，IATTP在当前状态下的结果也更可取。实验结果证明 IATTP 在解决优化问题方面具有竞争力。