• Journal of Power Electronics
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• 제16권1호
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• pp.287-296
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• 2016

Conventional maximum power point tracking (MPPT) methods are ineffective under partially shaded conditions because multiple local maximum can be exhibited on power-voltage characteristic curve. This study proposes an improved cuckoo search (ICS) MPPT method after investigating the cuckoo search (CS) algorithm applied in solving multiple MPPT. The algorithm eliminates the random step in the original CS algorithm, and the conception of low-power, high-power, normal and marked zones are introduced. The adaptive step adjustment is also realized according to the different stages of the nest position. This algorithm adopts the large step in low-power and marked zones to reduce search time, and a small step in high-power zone is used to improve search accuracy. Finally, simulation and experiment results indicate that the promoted ICS algorithm can immediately and accurately track the global maximum under partially shaded conditions, and the array output efficiency can be improved.

• IEIE Transactions on Smart Processing and Computing
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• 제4권2호
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• pp.115-125
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• 2015

This paper presents a Cuckoo search algorithm to secure adversaries misdirecting multi-hop routing in Mobile ad hoc networks (MANETs) using a robust Trust Predicated Routing Framework with an optimized cluster head selection. The clustering technique designed in this framework leads to efficient routing in MANETs. The heavy work load in the node causes an energy drop in cluster head, which leads to re-clustering of the group, and another cluster head is selected to avoid packet loss during data transmission. The problem in the re-clustering process is that the overall efficiency of the routing process is reduced and the processing time is increased. A Cuckoo search based optimization algorithm is proposed to solve the problem of re-clustering by selecting the secondary cluster head within the initially formed cluster group and eliminating the reclustering process. The proposed framework enables a node to select a reliable and secure route for MANET and the performance can be evaluated by comparing the simulated results with the AODV routing protocol, which shows that the performance of the proposed routing protocol are improved significantly.

• 한국산학기술학회논문지
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• 제18권4호
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• pp.334-340
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• 2017

신뢰도는 열차, 비행기, 여객선과 같이 시스템에 한번 고장이 발생한 경우 치명적인 결과로 이어져 시스템에서 중요한 설계 요인으로 고려되어진다. 상당히 높은 신뢰도를 요구하는 시스템에서 시스템의 신뢰도를 향상시키는 방법에는 다양하게 있지만, 부품의 중복은 시스템 신뢰도를 향상시키기 위한 효율적인 방법으로 알려져 있다. 신뢰도를 높이기 위해 부품을 중복하는 경우에는 어떤 부품을 몇 개를 중복해야 하는지를 시스템 신뢰도 측면과 비용, 기타 설계자원 측면에서 고려하여야 한다. 본 연구에서는 직렬 구조를 가지는 다계층 시스템에 대한 중복할당의 방법을 다룬다. 다계층시스템에 대한 정의를 설명하고, 제약된 설계비용에서 시스템 신뢰도를 최대화하기 위한 중복 부품의 선정과 중복수량을 최적화하는 방법을 다룬다. 특히, 다계층 시스템에서 경로집합 중에 단 하나의 품목만 중복이 가능한 경우에 대해서 다루며, 유효한 해를 찾기 위한 방법을 제시한다. 최적화를 위해 뻐꾸기 탐색 알고리즘을 적용한다. 뻐꾸기 탐색 알고리즘에서는 다계층시스템의 중복할당 최적화를 위한 탐색절차, 이웃해의 탐색 방법, 해의 표현 등을 제시한다. 수치예제를 통해 기존에 유전알고리즘과 뻐꾸기 탐색 알고리즘의 성능을 비교한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.433-440
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• 2014

In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and $CO_2$ emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.