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chaotic genetic algorithm-Based Forest Harvest Adjustment

Journal of Donghua University(English Edition) 2010年第2期27卷 148-151页

作者：李金铭王梅芳 College of Computer and Information Fujian Agriculture and Forestry University

Forest harvesting adjustment is a decision-making,large and complex system. In this paper,we analysis the shortcomings of the traditional harvest adjustment problems,and establish the model of multi-target harvest adjustment. As intelligent optimization,chaotic genetic algorithm has the parallel mechanism and the inherent global optimization characteristics which are suitable for multi-objective planning the settlement of the issue,specially in complex occasions where there are many objective functions and optimize variables. In order to solve the problem of forest harvesting adjustment,this paper introduces a genetic algorithm to the Forest Farm of Qiujia Liancheng Longyan for forest harvesting adjustment firstly. And the experimental result shows that the method is feasible and effective,and it can provide satisfactory solution for policy makers.

关键词： forest harvest adjustment multi-objective planning chaotic genetic algorithm optimal model

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chaotic genetic algorithm for Mixed Integer Programming Problem 3

Chaotic Genetic Algorithm for Mixed Integer Programming Prob...

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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Wang, Y. M. Zhang, Z. Zheng, J. B. Tong, L. L. Zhejiang Sci Tech Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783038352679

This paper proposed a chaotic genetic algorithm (CGA) to solve the mixed integer programming problem (MIPP). The basic idea of this algorithm is to overcome the deficiency of genetic algorithm (GA) by introducing chaotic disturbances into the genetic search process. Two typical MIPP problems are used to evaluate the performances of the proposed CGA. Experimental results show that performances of the algorithm have been improved by the chaotic disturbances, such as, search ability, precision, stability and convergence speed or calculation efficiency. The proposed CGA algorithm is suitable for solving complicated practical MIPP problem.

关键词： chaotic genetic algorithm mixed integer programming problem chaotic

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Cognitive radio resource allocation based on coupled chaotic genetic algorithm

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Chinese Physics B 2010年第11期19卷 704-711页

作者：俎云霄周杰曾昶畅 School of Electronic Engineering Beijing University of Posts and Telecommunications

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

关键词： cognitive radio chaotic genetic algorithm resource allocation coupled Logistic map

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Optimization of hybridization of artificial neuron with chaotic genetic algorithm in prediction process/with the application

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SOFT COMPUTING 2023年 1-9页

作者： Thalabi, Sahera Hussein Zain A. L. Heydari, Ali Akbar Tavakoli, Masoud Univ Tabriz Fac Math Stat & Comp Sci Dept Stat 29 Bahman Blvd Tabriz East Azerbaijan Iran Birjand Univ Technol Dept Sci Birjand South Khorasan Iran

Interest in the subject of prediction has increased in recent years, and modern and advanced methods have emerged, including neural network models, chaotic algorithms, hybrid algorithms, and others. All of these methods are able to learn and self-adapt to any model and do not need assumptions for the nature of the time series. Taking more than one method may lead to an increase in the accuracy of future estimates, so a neural network and chaotic genetic algorithms are used, so a model combines the strengths of two techniques to develop a hybrid CGANN model to obtain more accurate prediction results and reduce prediction errors. The aim of this study is to shed light on some of the statistical methods used in predicting the future demand for electric power in the southern region, as well as pointing to the most accurate methods in predicting the future of energy. The annual electrical energy consumption data for the southern region were used to make a comparison through practical application, and it was found that the three methods are very high accuracy and suitable for use in the prediction process, but the most accurate models are the hybrid algorithm consisting of neural networks and the chaotic genetic algorithm CGANN that gives better and more results efficient and thus, was used to predict the electrical load of the southern region up to 2032.

关键词： chaotic genetic algorithm Artificial neural network Activation functions chaotic map operator Hybrid network

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An Improved Multi-hop LEACH Protocol Based on chaotic genetic algorithm for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期134卷 1843-1862页

作者： Chuhang, Wang Huangshui, Hu Tingting, Wang Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Peoples R China Univ Technol & Engn Jilin Prov Smart Hlth Joint Innovat Lab New Genera Changchun 130012 Peoples R China Changchun Univ Technol Coll Comp Sci & Engn Changchun 130012 Peoples R China

Clustering in LEACH and its successors has been proved to be effective for not only improving energy efficiency but also extending network lifetime of wireless sensor networks. However, minimization of network energy consumption is still the most important topic in the research of hierarchical protocols based on LEACH. In this paper, an improved multi-hop LEACH protocol based on chaotic genetic algorithm (ICGA-LEACH) is proposed to obtain the optimal solution for energy efficiency and load balance at the same time. In ICGA-LEACH, cluster heads (CHs) are selected by a modified probability equation similar to LEACH, and then chaotic genetic algorithm is used to find the optimal routing paths and cluster members for the CHs according to a new constructed fitness function. Additionally, an adaptive round time is presented to further reduce energy consumption and prolong network lifetime. Simulation results in Matlab indicate that ICGA-LEACH is significantly superior to the existing relevant counterparts.

关键词： Wireless sensor networks Multi-hop routing chaotic genetic algorithm LEACH

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Information Gain with chaotic genetic algorithm for Gene Selection and Classification Problem

Information Gain with Chaotic Genetic Algorithm for Gene Sel...

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IEEE International Conference on System, Man, and Cybernetic

作者： Yang, Cheng-San Chuang, Li-Yeh Li, Jung-Chike Yang, Cheng-Hong Natl Cheng Kung Univ Inst Biomed Engn Tainan 70101 Taiwan I Shou Univ Dept Chem Engn Kaohsiung Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781424423835

For microarray data classification problem, selecting relevant genes from microarray data pose a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved and the usually small sample size. In order to correctly analyze microarray data, the goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed to select the relevant genes, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier. chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates.

关键词： microarray data feature selection information gain chaotic genetic algorithm K-nearest neighbor

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Real-Time System Fault-Tolerant Scheme Based on Improved chaotic genetic algorithm 10th

Real-Time System Fault-Tolerant Scheme Based on Improved Cha...

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10th European-Alliance-for-Innovation (EAI) International Conference on Wireless and Satellite Systems (WiSATS)

作者： Wang, Jie Kang, Junjie Hou, Gang Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Ubiquitous Network & Serv Software Lab Liaoning Dalian 116620 Peoples R China

ISBN: (纸本)9783030191566;9783030191559

Traditional evolutionary fault-tolerant scheme can effectively repair circuit faults, but for large-scale integrated circuits, the evolution process consumes a lot of time and it is difficult to meet the real-time requirements. In this paper, a real-time system fault-tolerant scheme based on improved chaotic genetic algorithm is proposed. The scheme uses a built-in test detection mechanism with feedback to detect the running state of the circuit in real time. When a fault occurs, normal system operation is maintained by the fault compensation mechanism. At the same time, the system uses the evolution repair mechanism to repair the faulty circuit. Evolution process uses an improved chaotic genetic algorithm, which can quickly converge to obtain a repair circuit through adaptive chaotic crossover and mutation. This paper builds a fault-tolerant system on the FPGA. In the experiment, the fault is randomly injected into circuit so that to simulate the actual circuit fault. The proposed algorithm and fault-tolerant scheme are used to verify the self-repairing ability of the system. The experimental results show that under real-time constraints, the repair rate of the fault circuit reaches 94%.

关键词： Fault-tolerant Evolution hardware chaotic genetic algorithm

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Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a chaotic genetic algorithm

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IEEE ACCESS 2020年 8卷 158082-158096页

作者： Wang, Chuhang Liu, Xiaoli Hu, Huangshui Han, Youjia Yao, Meiqin Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Peoples R China Jilin Univ Architecture & Technol Coll Comp Sci & Engn Changchun 130114 Peoples R China Jilin Univ Technol Coll Comp Sci & Engn Changchun 130012 Peoples R China

In wireless sensor networks, organizing nodes into clusters, finding routing paths and maintaining the clusters are three critical factors that significantly impact the network lifetime. In this paper, using a chaotic genetic algorithm, a clustering routing protocol combined with these three features called CRCGA is proposed to improve the network energy efficiency and load balancing. In CRCGA, the chaotic genetic algorithm is used to select the best cluster heads (CHs) and to find the optimal routing paths by coding them into a single chromosome simultaneously. chaotic genetic operators based on a novel fitness function considering minimum energy consumption and load balancing along with new determination conditions make the algorithm converge quickly. Besides, an adaptive round time considering energy and load balancing is presented to maintain the clusters so as to further reduce energy consumption. Simulation results indicate that CRCGA is better than LEACH, GECR, OMPFM and GADA-LEACH in terms of convergence speed, energy efficiency, load balancing, network throughput and lifetime.

关键词： Routing Biological cells genetic algorithms Energy consumption Wireless sensor networks Encoding Energy efficiency WSNs multi-hop routing chaotic genetic algorithm clustering energy and load balancing

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Clustered Routing Using chaotic genetic algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks

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SENSORS 2024年第13期24卷 4406页

作者： Khujamatov, Halimjon Pitchai, Mohaideen Shamsiev, Alibek Mukhamadiyev, Abdinabi Cho, Jinsoo Gachon Univ Dept Comp Engn Seongnam Si 1342 Gyeonggi Do South Korea Natl Engn Coll Dept Comp Sci & Engn Kovilpatti 627011 Tamil Nadu India Tashkent Univ Informat Technol Dept Data Commun Networks & Syst Tashkent 100200 Uzbekistan

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

关键词： chaotic genetic algorithm clustering energy efficiency grey wolf optimizer routing sensor networks

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Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on chaotic genetic algorithm

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SENSORS 2021年第13期21卷 4596-4596页

作者： Yang, Bin Huang, Mo Xie, Yao Wang, Changyuan Rong, Yingjiao Huang, Huihui Duan, Tao Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214035 Jiangsu Peoples R China

The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar's ground clutter data.

关键词： UCA radar ground clutter chaotic genetic algorithm clustering process characteristic factor

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