Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Huang, Xingwang; Zeng, Xuewen; Han, Rui

doi:10.1155/2017/3235720

Cited by 20 publications

(11 citation statements)

References 28 publications

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“…The bat algorithm (BA) was first presented in [22]. BA has also been successfully applied and used in many fields especially as optimization problem [24,25,26], clustering problem [27,28] and many other problems [29,30,31,32].…”

Section: Bat Algorithmmentioning

confidence: 99%

Bat-Genetic Encryption Technique

Mousa¹

2019

IJISA

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Nowadays, the security of confidential data is the vital issue in the digital world. Information security becomes even more essential in storing and transmitting data while online. For protecting digital data and achieving security and confidentiality over an insecure internet, the iterative Bat-Genetic Encryption Technique (B-GET) is proposed. The main stages of B-GET are preprocessing, encryption process, bat algorithm steps, and genetic processes. B-GET also comprises an arithmetic and logical operators that increase encryption quality. Empirical results show that the reconstructed data is a copy of the original. It also demonstrates that B-GET technique has a large space key and several defensive stages that resist many attacks and it has strong security based on multiple steps, multiple variables, and the main stages of the B-GET technique. Encrypted data is nearly random and does not contain any indication to secret data.

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Section: Bat Algorithmmentioning

confidence: 99%

Bat-Genetic Encryption Technique

Mousa¹

2019

IJISA

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“…It can be assumed that all weights and profits are nonnegative, and that all weights are not larger than the limited capacity C. Twelve test cases with different scales are considered to verify the optimization performance of A-AMBA for the knapsack problems. The typical test cases k 1 -k 5 [25], [26] are recorded as Table 9, where 'D' represents the dimension of knapsack problems, 'Parameters(w, p, C)' indicates the information of weight, profit and weight capacity, and 'Opt' presents the optimal value of corresponding knapsack problem. The cases k 6 -k 12 with large scales are designed to testify and compare the performance of the four comparative algorithms, using a random number generator.…”

Section: Zero-one Knapsack Problemmentioning

confidence: 99%

Amplitude Angle Modulated Bat Algorithm With Application to Zero-One Knapsack Problem

Huang

2019

IEEE Access

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Angle-modulated bat algorithm (AMBA) provides a technique that enables the bat algorithm (BA) developed for continuous problems to operate in binary spaces, which employs an angle modulation technique to generate a bit string corresponding to a binary problem solution and applies the BA to evolve the coefficients of the trigonometric generating function. However, there are some identified limitations of the original AMBA. This paper proposes a new AMBA variant called amplitude AMBA (A-AMBA). The A-AMBA is compared with the AMBA and several other binary heuristic algorithms on 13 classical benchmark functions and 12 zero-one knapsack problems. The obtained results indicate that the performance of this variant is superior to the AMBA in many problem cases. INDEX TERMS Angle modulation, bat algorithm, binary optimization, amplitude, zero-one knapsack problems.

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“…K-means is considered as the familiar algorithms for performing the cluster and it is a conventional distance based method and the distance is the measure of the similarity which define the short distance that tend to high similarity to Show all objects. Select K from the given N as the number of initial cluster center [27]. Measure the distance among each object and cluster center "m".…”

Section: Seed Selection Using Improved K-means Clustering Algorithm Wmentioning

confidence: 99%

“…They will reduce the loudness and increase the frequency of emitted ultrasonic sound, when bats chase preys. These characteristics of real bats help in establishing the BA [27]. These basic steps of BA have been mathematically described as follows.…”

Section: Seed Selection Using Improved K-means Clustering Algorithm Wmentioning

confidence: 99%

Improved K-Means with Adaptive Divergence Weight Bat Algorithm (IKM-ADWBA) and Feature Selection for Type 2 Diabetes Mellitus Prediction

2019

IJRTE

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Increase in blood glucose (hyperglycaemia) leads to Diabetes Mellitus. There are two kinds of Diabetes mellitus: (Type 1 Diabetes Mellitus (T1DM) and (Diabetes Mellitus (T2DM), then former one is dependent on insulin and the latter one is independent of insulin. Various factors make it difficult to diagnose it. SO the author focuses at binging-in and analyzing the method for making a novel robust diagnosis system using data mining methods. Complete datasets is necessary for data mining techniques, but these techniques doesn’t give accurate results with missing values and all features. So, for prediction, Handling Missing value replacement and selection of important features are becomes a major issue. Hence, Adaptive Neuro Fuzzy Inference System (ANFIS) were proposed to acquire the missing value in dataset and to rectify the above mentioned issue. Then for an effective seed selection in Improved K-means algorithm, Enhanced Inertia Weight Binary Bat Algorithm (EIWBBA) is proposed, which results in high convergence speed. This research work proposed for feature selection with the help of Improved Distributed Kernel based Principal Component analysis (IDKPCA) with less time, after minimizing the entire feature space to the best features set. Then for classification of clustered samples, the author brought-in the Support Vector Machine (SVM). The experimental result confirms that the proposed algorithm gives the best classification accuracy rate when compared with other methods. From Pima Indians Diabetes, the data set has been considered and the experiment is done with the help of MATLAB for examining the Knowledge and the results were distinguished with other outcomes using appropriate toolkits.

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Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

Cited by 20 publications

References 28 publications

Bat-Genetic Encryption Technique

Bat-Genetic Encryption Technique

Amplitude Angle Modulated Bat Algorithm With Application to Zero-One Knapsack Problem

Improved K-Means with Adaptive Divergence Weight Bat Algorithm (IKM-ADWBA) and Feature Selection for Type 2 Diabetes Mellitus Prediction

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