Time-varying Binary Phasmatodea Population Evolution Algorithm

被引：0

作者：

Lou, Jiayin ^{[1
]}

Chu, Shu-Chuan ^{[2
]}

Pan, Jeng-Shyang ^{[1
,2
,3
]}

Zhuang, Zhongjie ^{[3
]}

机构：

[1] Northeast Elect Power Univ, Coll Comp Sci, Jilin, Peoples R China

[2] Chaoyang Univ Technol, Dept Informat Management, Taichung, Taiwan

[3] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China

来源：

JOURNAL OF INTERNET TECHNOLOGY | 2025年 / 26卷 / 01期

关键词：

Swarm intelligence; Transfer function; Phasmatodea population evolution algorithm; 0-1 Knapsack problem; SWARM OPTIMIZATION ALGORITHM;

D O I：

10.70003/160792642025012601003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：25 / 42

页数：18

共 50 条

[41] Admissibility of time-varying observations for time-varying systems
Idrissi, A
Rhandi, A
JOURNAL OF COMPUTATIONAL ANALYSIS AND APPLICATIONS, 2004, 6 (03) : 229 - 241
[42] Surrogate-assisted Phasmatodea population evolution algorithm applied to wireless sensor networks
Liang, Lu-Lu
Chu, Shu-Chuan
Du, Zhi-Gang
Pan, Jeng-Shyang
WIRELESS NETWORKS, 2023, 29 (02) : 637 - 655
[43] Exponential decay for binary time-varying covariates in Cox models
Keown-Stoneman, Charles Donald George
Horrocks, Julie
Darlington, Gerarda
STATISTICS IN MEDICINE, 2018, 37 (05) : 776 - 788
[44] Marginal modeling of multilevel binary data with time-varying covariates
Miglioretti, DL
Heagerty, PJ
BIOSTATISTICS, 2004, 5 (03) : 381 - 398
[45] Surrogate-assisted Phasmatodea population evolution algorithm applied to wireless sensor networks
Lu-Lu Liang
Shu-Chuan Chu
Zhi-Gang Du
Jeng-Shyang Pan
Wireless Networks, 2023, 29 : 637 - 655
[46] Stability bound for time-varying uncertainty of positive time-varying discrete systems with time-varying delay time
Han H.-S.
Journal of Institute of Control, Robotics and Systems, 2016, 22 (06) : 424 - 428
[47] The evolution of structural balance in time-varying signed networks
Liu, Hua
Qu, Cunquan
Niu, Yawei
Wang, Guanghui
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 102 : 403 - 408
[48] An Adaptive Differential Evolution with Time-varying Inertia Weight
Jian, Li
ACC 2009: ETP/IITA WORLD CONGRESS IN APPLIED COMPUTING, COMPUTER SCIENCE, AND COMPUTER ENGINEERING, 2009, : 109 - 113
[49] Performance of the LMS algorithm on the estimation of time-varying channels
Galdino, JF
Pinto, EL
de Alencar, MS
2002 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2002, : 94 - 98
[50] Control of linear time-varying systems by the gradient algorithm
Chen, MS
PROCEEDINGS OF THE 36TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 1997, : 4549 - 4553

← 1 2 3 4 5 →