Architecture optimization of 4PUS+1PS parallel manipulator

Robotica ◽  
2010 ◽  
Vol 29 (5) ◽  
pp. 683-690 ◽  
Author(s):  
G. Abbasnejad ◽  
H. M. Daniali ◽  
A. Fathi
Keyword(s):  
Parallel Manipulator ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Passive Joint ◽  
A Minor ◽  
The Cost ◽  
Joint Limits ◽  
Architecture Optimization

SUMMARYThe main goal of this paper is the design of 4PUS+1PS parallel manipulator, using an optimization problem that takes into accounts the characteristics of the workspace and dexterity. The optimization problem is formulated considering constraints on actuated and passive joint limits. A comparison between quantum particle swarm Optimization (QPSO) and PSO is developed. Two numerical examples are presented, which reveal the advantages of QPSO to PSO. Moreover, it is shown that by introducing the dexterity index as a quality measure throughout the workspace, the parallel manipulator is improved at the cost of a minor reduction in its workspace.

scholarly journals Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guoqing Xia ◽  
Zhiwei Han ◽  
Bo Zhao ◽  
Xinwei Wang
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Unmanned Surface Vehicle ◽  
Kinematic Constraints ◽  
Quantum Particle Swarm Optimization ◽  
Local Path Planning ◽  
Local Path

An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4613
Author(s):  
Shah Fahad ◽  
Shiyou Yang ◽  
Rehan Ali Khan ◽  
Shafiullah Khan ◽  
Shoaib Ahmed Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Design Problems ◽  
Swarm Optimization ◽  
Electromagnetic Design ◽  
Quantum Particle Swarm Optimization ◽  
Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

scholarly journals Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sheetal N. Ghorpade ◽  
Marco Zennaro ◽  
Bharat S. Chaudhari ◽  
Rashid A. Saeed ◽  
Hesham Alhumyani ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Iot Applications

scholarly journals Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

2018 ◽  
Vol 10 (10) ◽  
pp. 3791 ◽  
Author(s):  
Daqing Wu ◽  
Jiazhen Huo ◽  
Gefu Zhang ◽  
Weihua Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Carbon Emissions ◽  
Time Window ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Transport Cost ◽  
Distribution Centers ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

scholarly journals Improved Quantum Particle Swarm Optimization for Mangroves Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Zhehuang Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Stable Convergence ◽  
Network Parameter ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Quantum Particle Swarm Optimization

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

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