An Improved Particle Swarm Optimization(PSO) Algorithm and Fuzzy Inference Systems Based Approach to Process Planning and Production Scheduling Integration in Holonic Manufacturing System (HMS)

Author(s):  
Fu-qing Zhao ◽  
Qiu-yu Zhang ◽  
Ya-hong Yang
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.


2013 ◽  
Vol 394 ◽  
pp. 505-508 ◽  
Author(s):  
Guan Yu Zhang ◽  
Xiao Ming Wang ◽  
Rui Guo ◽  
Guo Qiang Wang

This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.


2021 ◽  
Author(s):  
Yu Yang ◽  
Hongze Xu ◽  
Shaohua Li ◽  
Lingling Zhang ◽  
Xiuming Yao

Abstract Effective motion control can achieve accurate and fast positioning and movement of industrial robotics and improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics and can improve motion efficiency by providing high-speed and reasonable motion references to the motion controller. In this study, a new time-optimal TO strategy, polynomial interpolation function (PIF) combined with improved particle swarm optimization (PSO) considering kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator along a randomly assigned path. The 4-3-4 PIF is first used to generate the smooth and 3-order continuous movement trajectories of six joints in joint space. The PSO with cosine decreasing weight (CDW-PSO) algorithm further reduces the trajectories movement time considering the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque. Experimental results show that the CDW-PSO algorithm achieves a better convergence rate of 23 and a better fitness value of 2.46 compared with the PSO with constant weight and linearly decreasing weight algorithms. The CDW-PSO optimized movement time is reduced by 83.6% compared to the manually setting movement time value of 15. The proposed time-optimal TO strategy can be conducted easily and directly search for global optimal solutions without approximation of the limits. The optimized trajectories could be incorporated in the motion controller of the actual manipulators due to considering the kinematic and dynamic limits.


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