CMPSO:用于多任务无人机路径规划的新型协同进化多群粒子群优化技术

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gang Hu , Mao Cheng , Essam H. Houssein , Heming Jia
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引用次数: 0

摘要

为了应对无人飞行器(UVA)需要在多个地点执行任务的情况,本文提出了一种新的多任务无人飞行器路径规划模型,并提出了一种新的协同进化多组粒子群优化(CMPSO)来求解这一复杂模型。在该模型中,使用了一种新的球形曲线,即球 λ-Bezier 曲线(BλB)来表示无人机的路径。特别是,无人飞行器需要在必经点满足 G0 和 G1 连续性。以此为基础,我们建立了一个新模型,以生成安全、平滑且受爬升角和飞行高度限制的可行路径。为了有效地求解该模型,提出了由两种不同分组学习机制构成的 CMPSO 框架。两种不同的分组学习机制,即基于适配值和活动水平的分组,取代了 PSO 中原有的速度和位置更新方法。基于活动水平的分组机制以速度矢量模式的中位数为标准,将整个群体分为两组。它们有效地促进了粒子间的信息传递。此外,还引入了基于活动水平的突变机制,以解决 PSO 容易收敛到局部最优的缺陷。通过将CMPSO与2017年CEC上15种优秀的元启发式进行比较,CMPSO以3.72的平均排名位列第一。此外,在 21 个工程应用问题中,CMPSO 在 18 个问题上的表现最佳且最稳定。最后,CMPSO 被应用于路径规划模型的三种不同环境。在所有三种环境中,CMPSO 的成功率均为 100,优于其他比较过的算法。这显示了 CMPSO 在面对复杂路径规划问题时的效率和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning
To cope with the situation where an unmanned aerial vehicle (UVA) needs to perform missions to multiple locations, this paper presents a new multi-mission UAVs path planning model and proposes a novel co-evolutionary multigroup particle swarm optimization (CMPSO) for solving this complex model. In this model, a new ball curve, the ball λ-Bezier curve (BλB), is used to represent the path of UAVs. In particular, UAV needs to satisfy G0 and G1 continuity at the must-pass points. Using this as a basis, a new model is built to generate a feasible path that is safe, smooth and constrained by the angle of climb and flight altitude. To solve this model efficiently, CMPSO framed by two novel different grouping learning mechanisms is proposed. Two different group learning mechanisms, grouping based on fitness values and activity level, replace the original speed and position update methods in PSO. The grouping mechanism based on the activity level uses the median of the velocity vector modes as a criterion to divide the whole population into two. They effectively facilitate the transfer of information between particles. In addition, a mutation mechanism based on the activity level is introduced to address the defect of PSO’s proneness to converge to local optima. By comparing CMPSO with 15 excellent metaheuristics at CEC 2017, CMPSO is ranked first with an average ranking of 3.72. Also, CMPSO has the best and most stable performance on 18 of the 21 engineering application problems. Finally, CMPSO is applied to three different environments of the path planning model. CMPSO outperforms the other compared algorithms in all three environments with a success rate of 100. This shows the efficiency and practicality of CMPSO in facing complex path planning problems.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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