A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology

Zhang, Daren; Ma, Gang; Deng, Zhuoran; Wang, Qiao; Zhang, Guike; Zhou, Wei

doi:10.1016/j.asoc.2022.109660

Cited by 26 publications

(3 citation statements)

References 90 publications

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“…where g(x|X) is the acquisition function and X is the n observation points from f (x) so far. The expected improvement (EI) is a common choice as the acquisition function and it can be evaluated under the GP model as Equation ( 28) [39]:…”

Section: Approach Based On Generative Adversarial Imitation Learningmentioning

confidence: 99%

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

Lou,

Yuksek,

Inalhan

et al. 2024

Aerospace

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In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.

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Section: Approach Based On Generative Adversarial Imitation Learningmentioning

confidence: 99%

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

Lou,

Yuksek,

Inalhan

et al. 2024

Aerospace

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show abstract

“…Te objective optimization method has been efectively used in various areas of research; it originated from multiobjective optimization algorithms with exceptional performance, such as NSGA [35][36][37], PSO [38,39], and Jaya [40,41]. Among many multiobjective optimization algorithms, NSGA can ensure the uniform distribution of the nondominated optimal solution, the diversity of the population, and high computational efciency [42][43][44].…”

Section: Multiobjective Optimization Algorithmmentioning

confidence: 99%

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

Zhang

et al. 2023

Structural Control and Health Monitoring

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The global construction of rockfill dams has now surpassed the 300 m height level. Despite great achievements in dam design and construction, monitoring techniques have lagged behind the development of high rockfill dams. Existing deformation monitoring techniques are ill-suited to the high earth and water pressures, and extended monitoring periods are required for ultrahigh rockfill dams. This study introduces, for the first time, the use of a shape accel array (SAA) to monitor internal displacement in a 300 m high earth core rockfill dam. The SAA employs a rope-like array of capacitive MEMS accelerometers for deformation measurement. Compared to conventional monitoring techniques, SAA is a data-intensive monitoring technique. Based on the intensive data obtained from SAA, we employed a parameter inversion method, utilizing multiobjective optimization algorithm, the nondominated sorting genetic algorithm-III (NSGA-III), to inverse the constitutive model parameters of the rockfill dam. The multiobjective parameter inversion method maximizes the use of multisource monitoring data for predicting rockfill dam deformation.

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“…The swarm-based algorithm imitates the behaviors of animals preying or searching for foods to update their positions in the population [6], as Particle Swarm Optimization (PSO) [12] and Grey Wolf Optimizer (GWO) [13]. The swarm-based algorithm simulates the mechanism of division of labor and collaboration among animal populations in nature and has good application prospects for realistic and complex problems [14,15]. For instance, Zhao et al proposed the Manta Ray Foraging Optimization algorithm (MRFO) [16], which was inspired by the behaviors of the manta ray.…”

Section: Introductionmentioning

confidence: 99%

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

Ye,

Zhao,

Liu

et al. 2023

Computers, Materials &Amp; Continua

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The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is improved, moreover, a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation. IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems. The Wilcoxon Rank-sum Test, Friedman Test and Mann-Whitney U Test are conducted after emulation. The results indicate the competitiveness and merits of the IHBA, which has better solution quality and convergence traits. The source code is currently available from: https://github. com/zhaotao789/IHBA.

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A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology

Cited by 26 publications

References 90 publications

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

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