A novel hybrid Chaotic Aquila Optimization algorithm with Simulated Annealing for Unmanned Aerial Vehicles path planning

Ait-Saadi, Amylia; Meraihi, Yassine; Soukane, Assia; Ramdane-Chérif, Amar; Gabis, Asma Benmessaoud

doi:10.1016/j.compeleceng.2022.108461

Cited by 42 publications

(17 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the algorithm guarantees that each updated sand cat position lies between its current location and the prey. The mathematical representation of the prey attack phase is described by Equations ( 7) and (8).…”

Section: Hunting For Prey (Development)mentioning

confidence: 99%

“…Wang et al [7] presented an improved dynamic window approach (DWA) algorithm integrated with the 3D VFH+ algorithm to design a cooperative formation obstacle avoidance control algorithm, optimizing obstacle avoidance for UAV formation flight. Amylia et al [8] introduced a hybrid optimization scheme merging Chaotic Aquila Optimization with Simulated Annealing, resulting in improved performance in minimizing fitness values across different scenarios by striking a balance between exploitation and exploration.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Liu,

Lu,

Yang

et al. 2024

WEVJ

View full text Add to dashboard Cite

In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

show abstract

Section: Hunting For Prey (Development)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Liu,

Lu,

Yang

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…Moreover, due to their robust and reliable performance, meta-heuristic algorithms are also applied to solve other critical problems in power systems such as optimal reactive power planning [21], optimal scheduling of the wind-hydro-thermal power system [22], optimal power flow problem (OPF) [23], optimal placement of FACTS devices in power grid [24]. On top of that, the use of meta-heuristic algorithms can also be found in solving other problems, including the optimal drive system of three-phase VSI-Fed PMSM [25], structure design optimization [26], optimal path planning problem [27], feature selection [28].…”

Section: Introductionmentioning

confidence: 99%

Solving the economic load dispatch integrating clean energies in power system using Black Kite Algorithm

Quach Minh Thu,

Ta Minh Cuong

2024

World J. Adv. Eng. Technol. Sci.

View full text Add to dashboard Cite

This research applies a novel meta-heuristic algorithm called the Black Kite Algorithm (BKA) to solve the economic load dispatch integrating the clean energies (CE-ELD) with the main objective of minimizing the overall fuel cost (OFC) of all thermal power generating sources (TPGSs). A 20-TPGS power system is used to conduct the whole research and assess the actual performance of the BKA. While solving the considered problem, wind and solar generating sources are both integrated into the power system. Besides, power loss caused by the transmission process and different levels of load demand ranging from 2500, 2600, to 2700 MW are employed. The results achieved by BKA in three cases of load demand are compared with another meta-heuristic algorithm, the Coati optimization algorithm. The comparison between the results of the two methods indicates that BKA is completely superior to COZ in all criteria, especially in reaching the best values of OFC and stability throughout the test runs, regardless of the noticeable increase in load demand. Mainly, BKA always requires fewer iterations to reach the best OFC value, resulting in a lower fluctuation of OFC values among the test runs. Based on these results and evaluations, BKA is acknowledged to be the robust and stable search method, and we highly recommend using BKA to solve optimizations such as the CE-ELD problem.

show abstract

“…Q-learning algorithm penalizes significant deviations from the desired heading angle as compensation for environmental forces. In other hand, a chaotic algorithm helps explore state space after a learning period [5], [6]. Chaos algorithm is replaced with an epsilon-greedy selection strategy to make the most of the information gathered during the exploration phase.…”

Section: Introductionmentioning

confidence: 99%

Identify the Dynamic Position of Moving Vessels using Big Data and Deep Learning Approaches

Mistry¹,

Suaibia²,

Bin³

2023

EJECE

View full text Add to dashboard Cite

Recently, various purposes such as computer vision, self-driving vehicles, self-directed shipping, and so on. Therefore, machine learning and deep learning algorithms have demonstrated strong routine. Using machine learning applications, we can implement sophisticated functionality without relying on complex code constructions. An alternative is to train a learning system on a collected training dataset and ensure that it performs as expected. Two main benefits of a deep-learning-based system over a hard-coded one exist. A Reinforcement Learning, and deep learning-based system does not require such complex hard-coded algorithms, which makes it less prone to error and easier to implement. In this study we proposed a deep learning-driven systems are able to adapt to different situations by retraining their algorithms on collected data. Proposed method influenced varying obtain leaning to another example of changing conditions. Systems can adapt even when input distributions change over time.

show abstract

A novel hybrid Chaotic Aquila Optimization algorithm with Simulated Annealing for Unmanned Aerial Vehicles path planning

Cited by 42 publications

References 23 publications

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Solving the economic load dispatch integrating clean energies in power system using Black Kite Algorithm

Identify the Dynamic Position of Moving Vessels using Big Data and Deep Learning Approaches

Contact Info

Product

Resources

About