Interval Type-2 Fuzzy Vendor Managed Inventory System and Its Solution with Particle Swarm Optimization

2021 ◽  
Author(s):  
Zubair Ashraf ◽  
Deepika Malhotra ◽  
Pranab K. Muhuri ◽  
Q. M. Danish Lohani
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Inventory System ◽  
Vendor Managed Inventory ◽  
Swarm Optimization ◽  
Interval Type

scholarly journals Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization

2020 ◽  
Vol 10 (9) ◽  
pp. 3041
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Wall Following

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.

Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization

2019 ◽  
Vol 23 (3) ◽  
pp. 592-601 ◽  
Author(s):  
Hung Quoc Truong ◽  
◽  
Long Thanh Ngo ◽  
Long The Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Processing Speed ◽  
Particle Swarm ◽  
Improve Performance ◽  
Swarm Optimization ◽  
Local Optima ◽  
Gravitational Forces ◽  
Data Points ◽  
Interval Type

The interval type-2 fuzzy possibilistic C-means clustering (IT2FPCM) algorithm improves the performance of the fuzzy possibilistic C-means clustering (FPCM) algorithm by addressing high degrees of noise and uncertainty. However, the IT2FPCM algorithm continues to face drawbacks including sensitivity to cluster centroid initialization, slow processing speed, and the possibility of being easily trapped in local optima. To overcome these drawbacks and better address noise and uncertainty, we propose an IT2FPCM method based on granular gravitational forces and particle swarm optimization (PSO). This method is based on the idea of gravitational forces grouping the data points into granules and then processing clusters on a granular space using a hybrid algorithm of the IT2FPCM and PSO algorithms. The proposed method also determines the initial centroids by merging granules until the number of granules is equal to the number of clusters. By reducing the elements in the granular space, the proposed algorithms also significantly improve performance when clustering large datasets. Experimental results are reported on different datasets compared with other approaches to demonstrate the advantages of the proposed method.

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