scholarly journals Erratum to: Optimization of Construction Material Cost through Logistics Planning Model of Dragonfly Algorithm – Particle Swarm Optimization

2021 ◽  
Vol 25 (12) ◽  
pp. 4942-4942
Author(s):  
Pham Vu Hong Son ◽  
Nguyen Huynh Chi Duy ◽  
Pham Ton Dat
Keyword(s):  
Particle Swarm Optimization ◽  
Construction Material ◽  
Particle Swarm ◽  
Planning Model ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Material Cost ◽  
Logistics Planning

A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

2019 ◽  
Vol 233 (16) ◽  
pp. 6019-6031
Author(s):  
Jing Zhang ◽  
Wu Yu ◽  
Xiangju Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Model Simulation ◽  
Particle Swarm ◽  
Planning Model ◽  
Swarm Optimization ◽  
Multi Phase ◽  
Multi Mode

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

3D printing build orientation optimization for flexible support platform

2020 ◽  
Vol 26 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Hongyao Shen ◽  
Xiaoxiang Ye ◽  
Guanhua Xu ◽  
Linchu Zhang ◽  
Jun Qian ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
3D Printing ◽  
Particle Swarm ◽  
Support Structure ◽  
Swarm Optimization ◽  
Content Type ◽  
Build Orientation ◽  
Flexible Support ◽  
Material Cost ◽  
Printing Time

Purpose During the 3D printing process, the model needs to add a support structure to ensure structural stability. Excessive support structure reduces printing efficiency and results in material cost. A flexible support platform for 3 D printing has been designed. It can form an external support structure to replace the original support structure. This paper aims to study the influence of a model’s build orientation on properties when the model is printed on the platform, aiming to provide users with suitable solutions. Design/methodology/approach A fitness function for estimating the support structure with a support length is constructed. The particle swarm optimization (PSO) algorithm is modified and applied to find the build orientation that minimizes the support structure. However, when the model is printed on the platform, the build orientation of the minimum support structure enhances the complexity of the working path, resulting in an increase of printing time, which needs to be avoided. This paper applies a multi-objective particle swarm optimization (MOPSO) algorithm to minimize the support structure while minimizing printing time. The Pareto solution is obtained by the algorithm. Findings It is found that the model that has the cantilever structure can reduce more support structure after optimization on the platform, when there is surface quality requirement. When there is no limit, the modified algorithm can minimize the support structure of each model. Considering support structure and printing time, the MOPSO algorithm can easily get optimization results to guide the practical work. Originality/value This paper optimizes the model’s build orientation on the flexible support platform by PSO, thereby reducing material cost and improving work efficiency.

scholarly journals Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Bowei Xu ◽  
Junjun Li ◽  
Yongsheng Yang ◽  
Octavian Postolache ◽  
Huafeng Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Cost Efficiency ◽  
Network Planning ◽  
Particle Swarm ◽  
Coverage Rate ◽  
Planning Model ◽  
Swarm Optimization ◽  
Frequency Identification

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

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