蚁群优化算法
差异进化
数学优化
元启发式
运动规划
计算机科学
稳健性(进化)
路径(计算)
全局优化
趋同(经济学)
最优化问题
算法
人工智能
数学
机器人
基因
经济
化学
程序设计语言
生物化学
经济增长
作者
Zain Anwar Ali,Zhangang Han,Di Zhengru
标识
DOI:10.1177/0020294020915727
摘要
Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.
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