Quasi-Affine Transformation Evolutionary Algorithm With Communication Schemes for Application of RSSI in Wireless Sensor Networks

Du, Zhi-Gang; Pan, Jeng‐Shyang; Chu, Shu-Chuan; Luo, Hanjiang; Hu, Pei

doi:10.1109/access.2020.2964783

Cited by 79 publications

(42 citation statements)

References 39 publications

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“…Artificial neural networks show good performance in the analysis and prediction of complex nonlinear systems [4,[25][26][27][28]33]. Many scholars at home or abroad have put forward many kinds of traffic flow prediction methods based on artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm-Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction. INDEX TERMS QGA, LVQ neural network, short-term traffic flow prediction, global optimization.

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Section: Introductionmentioning

confidence: 99%

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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“…The evolution process of the entire group can be regarded as an optimization process, but the evolution trajectory of a single individual may not be an optimization process. Evolutionary calculations include Genetic Algorithm (GA) [13], Particle Swarm Optimization (PSO) [14][15][16][17], Grey Wolf Optimizer (GWO) [18][19][20][21], Cat Swarm Optimization [22][23][24], Differential Evolution (DE) [25][26][27], Ant Colony Optimization (ACO) [28][29][30], Artificial Bee Colony (ABC) [31,32], Flower Pollination Algorithm (FPA) [33,34], Bat Algorithm (BA) [35][36][37], QUasi-Affine TRansformation Evolutionary (QUATRE) [38][39][40][41], and Multi-Verse Optimizer (MVO) [42,43]. The pigeon swarm algorithm is a new meta-heuristic algorithm proposed by Duan in 2014 [44].…”

Section: Introductionmentioning

confidence: 99%

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.

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“…Optimization techniques are used to solve problems intelligently by choosing the optimal solution from a large number of solutions [1]. The meta-heuristic algorithm is viral for solving optimization problems as it is robust and straightforward [2][3][4], for example, reactive power planning problem in power systems [5], capacitated vehicle routing problem [6], and route planning of part process in flexible manufacturing systems [7]. Meta-heuristic algorithms have developed rapidly over the past few decades [8].…”

Section: Introductionmentioning

confidence: 99%

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

et al. 2020

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The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

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Quasi-Affine Transformation Evolutionary Algorithm With Communication Schemes for Application of RSSI in Wireless Sensor Networks

Cited by 79 publications

References 39 publications

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

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