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phasmatodea population evolution algorithm Based on Spiral Mechanism and Its Application to Data Clustering

Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized phasmatodea population evolution algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard phasmatodea population evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Time-varying Binary phasmatodea population evolution algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2025年第1期26卷 25-42页

作者： Lou, Jiayin Chu, Shu-Chuan Pan, Jeng-Shyang Zhuang, Zhongjie Northeast Elect Power Univ Coll Comp Sci Jilin Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

phasmatodea population evolution algorithm (PPE) is an optimization algorithm based on insect behavior. It excels in solving tasks in continuous space. Standard PPE is not suitable for addressing binary problems such as path selection problems, neural network training, and feature selection problems. However, real-world binary problems cannot be solved by the original PPE algorithm. Because binary solutions can only have values of 0 or 1, while the solution space of standard PPE is continuous. To address these issues, we propose the Binary phasmatodea population evolution (BPPE) algorithm and Time-Varying Binary phasmatodea population evolution (TV-BPPE) for dealing with issues with binary properties and study the effect of different transfer functions on the algorithm's performance.

关键词： Swarm intelligence Transfer function phasmatodea population evolution algorithm 0-1 Knapsack problem

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The phasmatodea population evolution algorithm and Its Application in 5G Heterogeneous Network Downlink Power Allocation Problem

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JOURNAL OF INTERNET TECHNOLOGY 2021年第6期22卷 1199-1213页

作者： Pan, Jeng-Shyang Song, Pei-Cheng Pan, Chun-An Abraham, Ajith Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China Univ Sydney Sch Architecture Design & Planning Sydney NSW Australia Machine Intelligence Res Labs MIR Labs Auburn WA USA

This paper proposes a new population-based heuristic algorithm called phasmatodea (stick insect) population evolution (PPE) algorithm. The implementation of the algorithm is inspired by the evolutionary characteristics of stick insect population. Stick insects will change their shape to adapt to the surrounding environment, and stick insect populations will also evolve as the environment changes. This paper implements the PPE algorithm based on the relevant characteristics of the stick insect and the population model. The PPE algorithm is compared with the classic and newer algorithms on 30 benchmark functions. The results show that the proposed algorithm has strong competitiveness. Then this paper tries to apply the proposed algorithm to the downlink power allocation problem in the fifth-Generation (5G) heterogeneous network, and has achieved certain results.

关键词： phasmatodea population evolution algorithm Two-layer heterogeneous network Downlink power allocation problem population competition model population growth model

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Simplified phasmatodea population evolution algorithm for optimization

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COMPLEX & INTELLIGENT SYSTEMS 2022年第4期8卷 2749-2767页

作者： Song, Pei-Cheng Chu, Shu-Chuan Pan, Jeng-Shyang Yang, Hongmei Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the phasmatodea (stick insect) population, called the phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

关键词： phasmatodea population evolution algorithm population competition model population growth model Optimization problems

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Multi-strategy Parallel phasmatodea population evolution algorithm for Public Transport Scheduling 17th

Multi-strategy Parallel Phasmatodea Population Evolution Alg...

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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Zhu, Yunxiang Yan, Fengting Pan, Jeng-Shyang Wan, Weibing Huang, Bo Shi, Zhicai Shanghai Univ Engn Sci Shanghai 201620 Peoples R China Shandong Univ Sci & Technol Qingdao 266590 Shandong Peoples R China

ISBN: (纸本)9789811910531;9789811910524

The phasmatodea population evolution algorithm (PPE) is a novel algorithm to simulate population evolution. In this paper, a multi-strategy parallel phasmatodea population evolution algorithm (MPPPE) is proposed to further improve the comprehensive performance of PPE. The step factor of the flower pollination algorithm is introduced to maintain the diversity of population. In terms of communication, two communication strategies are adopted, which not only speeds up the convergence rate but also improves the exploration ability of the algorithm. We compared this algorithm with the other three optimization algorithms in the CEC2013 test function and proved the excellent performance of this algorithm. Finally, this algorithm is applied to the bus scheduling problem in traffic transportation, and satisfactory optimization results are obtained.

关键词： Bus scheduling Flower pollen algorithm Parallel communication phasmatodea population evolution algorithm

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An Image Encryption Method Based on Logistic Mapping and phasmatodea population evolution algorithm 17th

An Image Encryption Method Based on Logistic Mapping and Pha...

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作者： Sun, Xiao-xue Song, Pei-Cheng Shieh, Chin-Shiuh Tsai, Pei-Wei Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Swinburne Univ Technol Melbourne Vic Australia

ISBN: (纸本)9789811910579;9789811910562

The security of information is closely related to our daily life and occupies an important position. The data encryption algorithm is an indispensable element in modern life. Image encryption technology is one of the more commonly used technologies and methods in contemporary networks. Since the logistic transformation is sensitive to the initial value, the paper tries to employ the phasmatodea population evolution (PPE) algorithm to train and select an optimal appropriate initial value to improve security. After the encryption simulation experiment test, the PPE algorithm in this paper has more outstanding encryption performance among several encryption methods, has better scrambling and diffusion effects, and has improved information security.

关键词： Chaos Image encryption Logistic mapping phasmatodea population evolution algorithm

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Hybrid algorithm optimization for coverage problem in wireless sensor networks

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TELECOMMUNICATION SYSTEMS 2022年第1期80卷 105-121页

作者： Jia, Han-Dong Chu, Shu-Chuan Hu, Pei Kong, LingPing Wang, XiaoPeng Snasel, Vaclav Jiang, Tong-Bang Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Nanyang Inst Technol Sch Software Nanyang 473004 Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic Dalian Maritime Univ Dept Intelligence Sci & Technol Dalian 116026 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

With the continuous development of evolutionary computing, many excellent algorithms have emerged, which are applied in all walks of life to solve various practical problems. In this paper, two hybrid fish, bird and insect algorithms based on different architectures are proposed to solve the optimal coverage problem in wireless sensor networks. The algorithm combines the characteristics of three algorithms, namely, particle swarm optimization algorithm, phasmatodea population evolution algorithm and fish migration optimization algorithm. The new algorithm has the advantages of the three algorithms. In order to prove the effectiveness of the algorithm, we first test it on 28 benchmark functions. The results show that the two hybrid fish, bird and insect algorithms with different architectures have significant advantages. Then we apply the proposed algorithm to solve the coverage problem of wireless sensor networks through experimental simulation. The experimental results show the advantages of our proposed algorithm and prove that our proposed hybrid fish, bird and insect algorithm is suitable for solving the coverage problem of wireless sensor networks.

关键词： evolutionary computation phasmatodea population evolution algorithm Fish migration optimization Coverage problem Wireless sensor network

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Improved butterfly optimization algorithm applied to prediction of combined cycle power plant

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 337-353页

作者： Wang, Xiao Sun, Xiao-Xue Chu, Shu-Chuan Watada, Junzo Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

The electricity output is worth monitoring because of the rising electricity demand. Ambient temperature, air pressure, relative humidity, and exhaust pressure all impact the production output of a combined cycle power plant. This study proposes the BOAPPE algorithm, which combines the butterfly optimization algorithm (BOA) with the phasmatodea population evolution algorithm (PPE) to estimate power output better and reduce excessive cost waste. When used in conjunction with a support vector regression (SVR) model, such as BOAPPE-SVR, for estimating the power output of a power plant under basic load, it not only enhances the model's prediction accuracy but also successfully avoids the problem of the model entering local optimization too soon. The parallel strategy of the BOAPPE algorithm in this research improves convergence speed, while the random walk strategy prevents the model from sliding into local optimization. The results reveal that the model paired with the BOAPPE algorithm is more accurate and better than the other models in this research when comparing the performance of the parameters such as mean square error, relative error, and correlation coefficient. As a result, the BOAPPE-SVR model is a viable model for power load forecasting. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Butterfly optimization algorithm phasmatodea population evolution algorithm Support vector regression Combined cycle power plant

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Optimization model of a ring-grid distribution system based on a solid-state hydrogen storage station 8

Optimization model of a ring-grid distribution system based ...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Zhao, Yun Lu, Yuxin Cai, Ziwen Yin, Yanhe Zhong, Yi Xie, Bolin Chi, Yuquan Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Guangdong Power Grid Co Ltd. Zhongshan Power Supply Bureau Zhongshan China South China University of Technology School of Electric Power Engineering Guangzhou China

ISBN: (纸本)9798350328813

Solid-state hydrogen storage station is an excellent new energy storage mode, which has been applied on the ground in the engineering field. However, it still needs to be optimized in terms of practical application scenarios and payback cycle. Therefore, this paper proposes an optimization model of a ring-grid distribution system for a solid-state hydrogen storage station with photovoltaic power generation (RHP). Distributed photovoltaic power generation is common on the rooftops of villages and factories, and the model integrates the characteristics of the solid-state hydrogen storage station with them, and uses the solid-state hydrogen storage station as the point of contact to loop the villages and factories in a low-voltage network, and establishes an optimization model with the lowest operating cost as the objective function. The results of the analysis based on typical data show that RHP improves the overall power supply reliability and operation economy of the distribution network, and has good business value and development prospects. © 2023 IEEE.

关键词： distribution systems optimization model phasmatodea population evolution algorithm solid-state hydrogen storage stations

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