A RFID Network Planning Method Based on Genetic Algorithm

Yang, Yanxi; Wu, Yujie; Xia, Min; Qin, Zhijing

doi:10.1109/nswctc.2009.238

Cited by 41 publications

(24 citation statements)

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“…In brief, the goal of RNP and its optimization can be summarized by the following statement: "to plan a cost efficient RFID network it is necessary to minimize the number of antennas, minimize interference of antennas and maximize coverage area of objects" [11,16]. Therefore, in this paper, to satisfy these targets and to design an efficient RFID network, a hybrid optimization technique is introduced.…”

Section: Hybrid Artificial Intelligence Optimization Techniquementioning

confidence: 99%

“…Tracking and identifying objects in these applications require the deployment of several RFID antennas in the RNP, 2 Complexity and the numbers of these antennas are calculated through the use of a mathematical model [10][11][12]. In the past, one of the typical ways to address the RNP problem was to use a trial and error approach, which was an inaccurate and inefficient solution for such an important issue [7,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, with the developments in computer technology, and software engineering, the conventional trial and error approach has been replaced with modern computational techniques that provide important criteria such as the coverage of objects, collision of antennas, and number of antennas [15]. Computational evolutionary techniques such as Artificial Neural Networks [16], Fuzzy Logic [17], Genetic Algorithms (GA) [10,11,18], particle swarm optimization (PSO) [13,19,20], differential evolution (DE) [9], and hierarchical artificial bee colony algorithm [8] are points of interest for many scientists working with the RNP problem. In this respect, Han and Jie [21] proposed a novel optimization algorithm, namely, the multicommunity GA-PSO, for solving the problem of complicated RNP.…”

Section: Introductionmentioning

confidence: 99%

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Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

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Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named "hybrid AI optimization technique," to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

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Section: Hybrid Artificial Intelligence Optimization Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

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“…[10][11][12] In the past, one of the typical ways to address the RNP problem was using a trial-and-error approach, which was an inaccurate and inefficient solution for such an important issue. 7,13,14 In addition, this approach could only be used on small-scale RNP problems.…”

Section: Introductionmentioning

confidence: 99%

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Azizi

Barenji

Hashmipour

2016

Advances in Mechanical Engineering

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Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

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“…However, the methods used in the previous studies to solve the multi-objective RNP (MORNP) are always weighted coefficient approaches used to transform multiple objectives into a single objective (Guan et al, 2006;Chen et al, 2011;Seok et al, 2010;Gong et al, 2012;Karaboga and Akay, 2009). Most of them are based on evolutionary algorithms (EAs) and swarm intelligence (SI) optimization techniques, such as genetic algorithms (GA) (Guan et al, 2006; Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/jnca Yang et al, 2009), evolutionary strategy (ES) (Bhattacharya and Roy, 2010), differential evolution (DE) (Seok et al, 2010), and particle swarm optimization (PSO) (Chen et al, 2011;Bhattacharya and Roy, 2010;Gong et al, 2012). Notice that these works considered only one object in RFID network planning or a single objective function linearly composed of several planning objectives, and none of them can generate the tradeoffs between objectives.…”

Section: Introductionmentioning

confidence: 99%

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Zhu

et al. 2014

Journal of Network and Computer Applications

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A RFID Network Planning Method Based on Genetic Algorithm

Cited by 41 publications

References 1 publication

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

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