Correlation between RFID Network Planning (RNP) Parameters and Particle Swarm Optimization (PSO) Solutions

Nawawi, Azli; Hasnan, Khalid; Bareduan, Salleh Ahmad

doi:10.4028/www.scientific.net/amm.465-466.1245

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Proposed in 1995, PSO was applied to optimization, biomedicine, communication, control, plan, prediction, filter, and parameter estimation in rainfall-runoff modeling and so forth [24][25][26][27][28][29][30]. It was improved in selecting the parameter, the velocity equation of the particle, uncertainty stimulation, learning abilities, stability, convergence, and more [31][32][33][34][35][36][37][38][39][40][41]. Wang et al and Chen et al applied PSO to solve optimization of a nine-work network plan [42,43].…”

Section: Introductionmentioning

confidence: 99%

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Zhang

Yang

2017

Mathematical Problems in Engineering

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No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

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Section: Introductionmentioning

confidence: 99%

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Zhang

Yang

2017

Mathematical Problems in Engineering

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“…Chen et al [7] developed an optimization model for planning the positions of the readers in the RNP based on PS2O. Nawawi et al [22] proposed a solution based on the PSO correlation between RNP parameters. Lu and Yu [23] proposed an approach based on -coverage for RFID Network Planning; a -coverage model is formulated from a multidimensional optimization problem and plant growth simulation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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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“…H Chen et al 7 developed an optimization model for planning the positions of the readers in the RNP based on PSO. Nawawi et al 22 proposed solution based on the PSO correlation between RNP parameters. S Lu and S Yu 23 proposed an approach based on k-coverage for RFID network planning, a k-coverage model is formulated from a multidimensional optimization problem and plant growth simulation algorithm, this is used to optimize the RFID networks by determining the optimal adjustable parameters.…”

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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Correlation between RFID Network Planning (RNP) Parameters and Particle Swarm Optimization (PSO) Solutions

Cited by 4 publications

References 17 publications

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

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

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