Abstract
The prominent shortcoming of the basic artificial raindrop algorithm in UAV route planning is easily trapped into local optimal solution. In the present work, the original artificial raindrop algorithm is improved. A Balwin-teaching-learning-based artificial raindrop algorithm (BTLARA) is proposed, whereby each raindrop updates itself by using the combination of its own unique mode and Balwin-teaching-learning-based optimization pattern operator. In order to demonstrate the effectiveness of this algorithm, the UAV route planning is utilized for simulation. According to the results, the algorithm proposed in this paper significantly enhances the convergence and can obtain higher-quality navigation trace and convergence, which enables it to better avoid threat paths.
Highlights
Optimization problems such as UAV route planning are quite common in the academia, and they have become an exciting new area of mathematical programming for several decades
based artificial raindrop algorithm (BTLARA) is compared with some other well-established teachinglearning-based optimization (TLBO) algorithms including teaching-learning-based optimization (TLBMO), harmony search and teaching-learning-based optimization (HSTLBO), teaching-learning-based optimization and differential evolution (TLBODE), and teaching and peer-learning particle swarm optimization (TPLPSO). e statistical mean value (Fmean) in Table 7 shows that BTLARA has the best overall performance
BTLARA outperforms the other three algorithms on functions F2, F3, F4, and F5, except for F1 and F6, on which BTLARA performs the same as the teaching-learning-based artificial raindrop algorithm (TLARA) in terms of Fmean. us, it can be concluded that BTLARA has the best overall performance as compared with the other well-established TLBO algorithms including TLBMO, HSTLBO, TLBODE, and TPLPSO
Summary
Optimization problems such as UAV route planning are quite common in the academia, and they have become an exciting new area of mathematical programming for several decades. E Baldwin learning strategy is applied to guarantee that fitter raindrops with higher probability are the likely winners selected In this way, it provides a more reliable searching direction towards the global optimal solution. E teachinglearning-based optimization algorithm introduced by Rao [9] is an optimization technique based on population, which is analogous to the teaching and learning process between the teacher and students. It has two steps, i.e., the teacher phase and the learner phase. The pseudocode and flowchart of the Balwin-teaching-learning-based artificial raindrop algorithm (BTLARA) is shown in Table 3 and, respectively The pseudocode and flowchart of the Balwin-teaching-learning-based artificial raindrop algorithm (BTLARA) is shown in Table 3 and in Figure 4, respectively
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