Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems

2019 ◽  
Author(s):  
Jeng-Shyang Pan ◽  
Jenn-Long Liu ◽  
Shou-Cheng Hsiung
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Aerial Vehicle ◽  
Vehicle Path ◽  
Planning Problems

scholarly journals Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lina Huo
Keyword(s):  
Genetic Algorithm ◽  
Particle Filter ◽  
Visual Tracking ◽  
Particle Filters ◽  
Time Window ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bayesian Probability ◽  
Vehicle Path

Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only introduces particle filters, which are advanced visual tracking, but also improves the cuckoo search algorithm; when the bird’s egg is found by the bird’s nest owner, it needs to randomly change the position of the entire bird’s nest, which speeds up the search speed of the optimal delivery route. Analyze and compare the hybrid intelligent algorithm and the cuckoo search algorithm. Finally, the international standard test set Benchmark Problems is used for testing. The experimental outcomes indicated that the new hybrid intelligent approach is an effective algorithm for handling vehicle routing tasks with time windows.

Quantum-entanglement pigeon-inspired optimization for unmanned aerial vehicle path planning

2018 ◽  
Vol 91 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Siqi Li ◽  
Yimin Deng
Keyword(s):  
Path Planning ◽  
Quantum Entanglement ◽  
Unmanned Aerial Vehicle ◽  
Quantum Effect ◽  
Market Place ◽  
Content Type ◽  
Aerial Vehicle ◽  
Stable Performance ◽  
Vehicle Path ◽  
Planning Problems

Purpose The purpose of this paper is to propose a new algorithm for independent navigation of unmanned aerial vehicle path planning with fast and stable performance, which is based on pigeon-inspired optimization (PIO) and quantum entanglement (QE) theory. Design/methodology/approach A biomimetic swarm intelligent optimization of PIO is inspired by the natural behavior of homing pigeons. In this paper, the model of QEPIO is devised according to the merging optimization of basic PIO algorithm and dynamics of QE in a two-qubit XXZ Heisenberg System. Findings Comparative experimental results with genetic algorithm, particle swarm optimization and traditional PIO algorithm are given to show the convergence velocity and robustness of our proposed QEPIO algorithm. Practical implications The QEPIO algorithm hold broad adoption prospects because of no reliance on INS, both on military affairs and market place. Originality/value This research is adopted to solve path planning problems with a new aspect of quantum effect applied in parameters designing for the model with the respective of unmanned aerial vehicle path planning.

Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

2017 ◽  
Vol 266 ◽  
pp. 445-457 ◽  
Author(s):  
Chen YongBo ◽  
Mei YueSong ◽  
Yu JianQiao ◽  
Su XiaoLong ◽  
Xu Nuo
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Aerial Vehicle ◽  
Vehicle Path

scholarly journals Optimized cuckoo search algorithm using tournament selection function for robot path planning

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142199613
Author(s):  
Kaushlendra Sharma ◽  
Shikha Singh ◽  
Rajesh Doriya
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Selection Function ◽  
Local Minima ◽  
Robot Path Planning ◽  
Tournament Selection ◽  
Robot Path

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

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