Optimizing Radio Frequency Identification Networks Planning by using Particle Swarm Optimization Algorithm with Fuzzy Logic Controller and Mutation

2017 ◽  
Vol 63 (5) ◽  
pp. 728-735 ◽  
Author(s):  
Fahimeh Zakeri ◽  
Mehdi Golsorkhtabaramiri ◽  
Mehdi Hosseinzadeh
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Optimization Algorithm ◽  
Radio Frequency Identification ◽  
Fuzzy Logic Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Frequency Identification

scholarly journals An optimization model for the operations of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401774801 ◽  
Author(s):  
Jianwei Ren ◽  
Chunhua Chen ◽  
Hao Xu ◽  
Qingqing Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Optimization Algorithm ◽  
Radio Frequency Identification ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Swarm Optimization ◽  
Frequency Identification

In a pallet pool, pallets would be delivered through a supply chain. The operation procedure that consists of at least five operation processes as distribution, reposition, recycling, purchase (or rent), and maintenance is quite complex. These pallets are likely to be damaged, lost, destroyed, and so on. So, it is necessary to monitor the pallets using radio-frequency identification technology. However, there is no literature on the management of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets being put into consideration. In our research, an optimization model is presented to manage such a pallet pool. The objective of the optimization model is to minimize the total operation cost of a pallet pool including distribution cost, reposition cost, recycling cost, purchase or rent cost, loss cost, maintenance cost, loading and unloading cost, storage cost, and punishment cost. A particle swarm optimization algorithm is developed in Microsoft Visual Basic. Our numerical example shows that the optimization model and particle swarm optimization algorithm are effective. It is proved that the model and algorithm also can be used to measure whether the investment of a radio-frequency identification system is valuable or not. We proposed some suggestions for the pallet pools management.

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.

scholarly journals The Optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on Particle Swarm Optimization Algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 99 ◽  
Author(s):  
Tien Tran
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Diesel Engine ◽  
Fuzzy Logic Control ◽  
Engine Speed ◽  
Fuzzy Logic Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Logic Control

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm

2020 ◽  
Vol 19 (01) ◽  
pp. 2050005 ◽  
Author(s):  
Sonia Chhabra ◽  
Harvir Singh
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Cost Estimation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Cost Driver ◽  
Swarm Optimization ◽  
Software Cost

Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm.

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