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Ai-Qing Tian
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

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Article
Published: 12 April 2021 in Applied Intelligence
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The Pigeon-Inspired Optimization (PIO) algorithm is an intelligent algorithm inspired by the behavior of pigeons returned to the nest. The binary pigeon-inspired optimization (BPIO) algorithm is a binary version of the PIO algorithm, it can be used to optimize binary application problems. The transfer function plays a very important part in the BPIO algorithm. To improve the solution quality of the BPIO algorithm, this paper proposes four new transfer function, an improved speed update scheme, and a second-stage position update method. The original BPIO algorithm is easier to fall into the local optimal, so a new speed update equation is proposed. In the simulation experiment, the improved BPIO is compared with binary particle swarm optimization (BPSO) and binary grey wolf optimizer (BGWO). In addition, the benchmark test function, statistical analysis, Friedman’s test and Wilcoxon rank-sum test are used to prove that the improved algorithm is quite effective, and it also verifies how to set the speed of dynamic movement. Finally, feature selection was successfully implemented in the UCI data set, and higher classification results were obtained with fewer feature numbers.

ACS Style

Jeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence 2021, 1 -19.

AMA Style

Jeng-Shyang Pan, Ai-Qing Tian, Shu-Chuan Chu, Jun-Bao Li. Improved binary pigeon-inspired optimization and its application for feature selection. Applied Intelligence. 2021; ():1-19.

Chicago/Turabian Style

Jeng-Shyang Pan; Ai-Qing Tian; Shu-Chuan Chu; Jun-Bao Li. 2021. "Improved binary pigeon-inspired optimization and its application for feature selection." Applied Intelligence , no. : 1-19.

Research article
Published: 10 June 2020 in International Journal of Distributed Sensor Networks
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In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-Pigeon-Inspired Optimization Algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary Algorithm for the Pigeon-Inspired Optimization Algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the Pigeon-Inspired Optimization algorithm and makes the Pigeon-Inspired Optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-Pigeon-Inspired Optimization algorithm into the Distance Vector–Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location.

ACS Style

Xiao-Xue Sun; Jeng-Shyang Pan; Shu-Chuan Chu; Pei Hu; Ai-Qing Tian. A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks. International Journal of Distributed Sensor Networks 2020, 16, 1 .

AMA Style

Xiao-Xue Sun, Jeng-Shyang Pan, Shu-Chuan Chu, Pei Hu, Ai-Qing Tian. A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks. International Journal of Distributed Sensor Networks. 2020; 16 (6):1.

Chicago/Turabian Style

Xiao-Xue Sun; Jeng-Shyang Pan; Shu-Chuan Chu; Pei Hu; Ai-Qing Tian. 2020. "A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 6: 1.

Journal article
Published: 20 March 2020 in Processes
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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.

ACS Style

Ai-Qing Tian; Shu-Chuan Chu; Jeng-Shyang Pan; Yongquan Liang. A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems. Processes 2020, 8, 356 .

AMA Style

Ai-Qing Tian, Shu-Chuan Chu, Jeng-Shyang Pan, Yongquan Liang. A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems. Processes. 2020; 8 (3):356.

Chicago/Turabian Style

Ai-Qing Tian; Shu-Chuan Chu; Jeng-Shyang Pan; Yongquan Liang. 2020. "A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems." Processes 8, no. 3: 356.

Journal article
Published: 21 January 2020 in Sustainability
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Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

ACS Style

Ai-Qing Tian; Shu-Chuan Chu; Jeng-Shyang Pan; Huanqing Cui; Wei-Min Zheng. A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station. Sustainability 2020, 12, 767 .

AMA Style

Ai-Qing Tian, Shu-Chuan Chu, Jeng-Shyang Pan, Huanqing Cui, Wei-Min Zheng. A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station. Sustainability. 2020; 12 (3):767.

Chicago/Turabian Style

Ai-Qing Tian; Shu-Chuan Chu; Jeng-Shyang Pan; Huanqing Cui; Wei-Min Zheng. 2020. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station." Sustainability 12, no. 3: 767.