Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning

Han, Zengliang; Shao, Shuyi; Wu, Qingxian

doi:10.1016/j.ast.2022.107374

Cited by 51 publications

(15 citation statements)

References 53 publications

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“…The geological factors mainly refer to the geological age and geomorphologic unit. The older the geological age, the better the degree of consolidation, compactness, and structure, and the stronger the anti-liquefaction ability [32][33][34].…”

Section: Burial Conditionsmentioning

confidence: 99%

Improved artificial electric field algorithm based on multi-strategy and its application

Tian

Liu

Wang

et al. 2022

IJCAI

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Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. To improve the global exploration ability and local development ability of artificial electric field algorithms, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm is combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient, and shear stress ratio. Compared with traditional methods such as the standard method and seed simplification method, the results show that the IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification.Povzetek: Predstavljen je izboljšan algoritem umetnega električnega polja na osnovi mnogoterih strategij.

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Section: Burial Conditionsmentioning

confidence: 99%

Improved artificial electric field algorithm based on multi-strategy and its application

Tian

Liu

Wang

et al. 2022

IJCAI

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“…This algorithm will take the shortest route to its destination while avoiding collisions with other drones. The algorithm can look for a different path to avoid a collision by setting a limit of the smallest permissible distance between UAVs 70 . An effective combinatorial algorithm for discrete applications is ACO.…”

Section: Characteristics Of Coverage Path Planning For Uavmentioning

confidence: 99%

“…The algorithm can look for a different path to avoid a collision by setting a limit of the smallest permissible distance between UAVs. 70 An effective combinatorial algorithm for discrete applications is ACO. A new ant colony model was developed for continuous space; however, it is relatively complicated.…”

Section: Shuffled Frog Leaping Algorithmmentioning

confidence: 99%

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Mannan

Obaidat

Mahmood

et al. 2022

Int J Communication

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The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.

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“…However, in complex flight environments, UAV path planning problems are often highdimensional. In addition, usually in the path planning stage, if the kinematic constraints are not considered, the path planned by the algorithm cannot fly in the actual situation [22][23][24]. There are many unprocessed track turning points and lack of continuous smoothness.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Lin

et al. 2022

IEEE Access

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Path planning is the key for unmanned aerial vehicle (UAV) to perform tasks efficiently, which needs to quickly obtain the optimal path in the complex environment. To solve the path planning problem in the complex urban environment, an improved artificial bee colony algorithm based on multistrategy synthesis (IABC) is proposed to generate the appropriate path for the UAV. The IABC is based on the hybrid mechanism of chaotic mapping and Pareto principle to initialize the population, so as to fully search the solution space and provide an approximate optimal flight path for UAV. Meanwhile, in order to balance exploration and development, two new search equations are designed to generate candidate solutions, which provide more superior flight paths for UAVs. In addition, tangent random evolution mechanism is added to enhance the strategy of updating the algorithm offspring, maximize the quality of flight path generation, and effectively solve the path planning problem. Aiming at the complex urban environment and the multi-constraint optimization problem of UAV flight, the IABC is combined with cubic spline interpolation to generate a smooth path to meet the UAV flight maneuver characteristics. The improved algorithm proposed in this paper has been compared with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Search Artificial Bee Colony (SABC) and Gbest Artificial Bee Colony (GABC). The algorithm proposed in this paper has good feasibility and effectiveness in solving the UAV path planning problem.INDEX TERMS artificial bee colony algorithm (ABC), path planning, unmanned aerial vehicle (UAV), cubic spline interpolation.

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Improved artificial bee colony algorithm-based path planning of unmanned autonomous helicopter using multi-strategy evolutionary learning

Cited by 51 publications

References 53 publications

Improved artificial electric field algorithm based on multi-strategy and its application

Improved artificial electric field algorithm based on multi-strategy and its application

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

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