Robust Q-learning-based multi-objective sheep flock optimizer with a Cauchy operator for effective path planning in unmanned aerial vehicles,International Journal of Communication Systems

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Robust Q-learning-based multi-objective sheep flock optimizer with a Cauchy operator for effective path planning in unmanned aerial vehicles
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2023-10-21 , DOI: 10.1002/dac.5641
Vikash Kumar ₁ , Seemanti Saha ₁

Affiliation

Unmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non-linearity, multi-modality, and inseparability. On the other hand, meta-heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q-learning-based multi-objective sheep flock optimizer with a Cauchy operator (Q-MOSFO-CA) to solve the constrained UAV path planning issues. The multi-objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q-learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively.

中文翻译：

基于鲁棒 Q 学习的多目标羊群优化器，采用柯西算子，用于无人机的有效路径规划

无人机 (UAV) 路径规划可以视为非确定性多项式 (NP) 难题或优化问题。由于不连续性、非线性、多模态和不可分离性，传统方法无法有效处理这些问题。另一方面，元启发式算法可以有效解决这些问题，因为它们简单、适应性强且无需推导。为了提高各种具有挑战性的环境下的性能，本文提出了一种新颖的基于 Q 学习的带有柯西算子的多目标羊群优化器（Q-MOSFO-CA）来解决受限无人机路径规划问题。这里考虑的多目标函数是成本和约束（威胁、地形、转弯、攀爬和滑行约束），以确定可行的最佳路径。为了避免陷入局部最优的可能性，解决收敛不平衡的缺点，同时保持开发和探索能力，将柯西算子（CA）与羊群优化（SFO）算法相结合。引入 Q-learning 模型来平衡全局搜索和局部搜索。这里，探索模型执行全局搜索，而开发模型执行局部搜索以获得最佳解决方案。在仿真场景中，在两种场景下进行统计分析，确定收敛迭代次数（NIC）、评估时间（ET）、能耗和收敛分析等一些基本指标。对于场景 1 和场景 2，所提出的方法分别获得 NIC 为 1305 和 1436，ET 为 12.8 和 15.2 s，能耗分别为 20,600 和 21,465 J。

更新日期：2023-10-21

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