Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate’s nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time.

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An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

Journal of Systems Science and Information ◽

10.1515/jssi-2014-0481 ◽

2014 ◽

Vol 2 (6) ◽

pp. 481-504 ◽

Cited By ~ 4

Author(s):

Xiangfei Li ◽

Zaisheng Zhang ◽

Chao Huang

Keyword(s):

Empirical Mode Decomposition ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Stock Index ◽

Support Vector ◽

Mode Decomposition ◽

Parameters Selection ◽

Predicted Values ◽

Improved Cuckoo Search Algorithm

AbstractIn order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms in parameters selection.

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Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Energies ◽

10.3390/en14123420 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3420

Author(s):

Yi Liang ◽

Haichao Wang

Keyword(s):

Search Algorithm ◽

Evaluation Model ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Support Vector ◽

Sustainability Evaluation ◽

Belt And Road ◽

Improved Cuckoo Search Algorithm ◽

Pair Analysis ◽

The Belt And Road

Scientific and timely sustainability evaluation of the photovoltaic industry along the Belt and Road is of great significance. In this paper, a novel hybrid evaluation model is proposed for accurate and real-time assessment that integrates modified set pair analysis with least squares support vector machine that combines improved cuckoo search algorithm. First, the indicator system is set from five principles, namely economy, politics, society, ecological environment and resources. Then, the traditional approach is established through modifying set pair analysis on the basis of variable fuzzy set coupling evaluation theory. A modern intelligent assessment model is designed that integrates improved cuckoo search algorithm and least squares support vector machine where the concept of random weight is introduced in improved cuckoo search algorithm. In the case analysis, the relative errors calculated by the proposed model all fluctuate in the range of [−3%, 3%], indicating that it has the strongest fitting and learning ability. The empirical analysis verifies the scientificity and precision of the method and points out influencing factors. It provides a new idea for rapid and effective assessment of PV industry along the Belt and Road, as well as assistance for the sustainable development of this industry. This paper innovatively proposes the sustainability evaluation index system and evaluation model for the photovoltaic industry in countries along the Belt and Road, thus contributing to the promotion of sustainable development of the photovoltaic industry in countries along the Belt and Road.

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Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm

IEEE Access ◽

10.1109/access.2020.2966712 ◽

2020 ◽

Vol 8 ◽

pp. 14642-14658 ◽

Cited By ~ 37

Author(s):

Zichen Zhang ◽

Wei-Chiang Hong ◽

Junchi Li

Keyword(s):

Search Algorithm ◽

Load Forecasting ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Support Vector ◽

Electric Load ◽

Variational Mode Decomposition ◽

Support Vector Regression Model ◽

Mode Decomposition ◽

Improved Cuckoo Search Algorithm

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Optimization of distribution network configuration with multi objective function based on improved cuckoo search algorithm

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.1886 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1685-1693

Author(s):

Thuan Thanh Nguyen

Keyword(s):

Objective Function ◽

Search Algorithm ◽

Distribution Network ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Multi Objective ◽

Search Mechanism ◽

Voltage Deviation ◽

Improved Cuckoo Search Algorithm

This paper proposes an improved cuckoo search (ICSA) for solving the distribution network reconfiguration (NR) problem with multi-objective function. The membership functions are considered consisting of minimizing of power loss, load balancing among branches and among the feeders, node voltage deviation and switching operation numbers. ICSA is developed from the original CSA with adding the local search mechanism for exploiting around the current best solution. The effectiveness of the ICSA has validated on the 70-node and the 83-node practical systems. The obtained results have been compared to those from runner root algorithm (RRA) and other methods in the literature. The obtained results demonstrate that ICSA has high ability for searching the optimal solution with higher successful rate and better quality of obtained solution as well as smaller iterations compared to RRA and other methods. Therefore, ICSA is a reliable method for the multi-objective NR problems.

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Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189134 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8125-8137

Author(s):

Jackson J Christy ◽

D Rekha ◽

V Vijayakumar ◽

Glaucio H.S. Carvalho

Keyword(s):

Intelligent Transportation System ◽

Search Algorithm ◽

Mac Protocol ◽

High Mobility ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Traffic Density ◽

Scheduling Problem ◽

Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01951-1 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Yang Wang ◽

Feifan Wang ◽

Yujun Zhu ◽

Yiyang Liu ◽

Chuanxin Zhao

Keyword(s):

Sensor Network ◽

Sensor Node ◽

Search Algorithm ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Sensor Nodes ◽

Optimization Strategy ◽

Multi Objective ◽

Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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