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Firefly algorithm with division of roles for complex optimal scheduling Research Articles

Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye,zhaojia925@163.com,chen_9731@163.com,rbxiao@hust.edu.cn,yejun68@sina.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 10,   Pages 1311-1333 doi: 10.1631/FITEE.2000691

Abstract: A single strategy used in the cannot effectively solve the complex problem. Thus, we propose the FA with (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy ; the developer chooses two leaders randomly and uses the strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of of cascade reservoirs.

Keywords： 萤火虫算法；角色分工；柯西突变；精英邻域搜索；优化调度    

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Finding map regions with high density of query keywords Article

Zhi YU, Can WANG, Jia-jun BU, Xia HU, Zhe WANG, Jia-he JIN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1543-1555 doi: 10.1631/FITEE.1600043

Abstract: We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search (DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.

Keywords： Map search     Region search     Region recommendation     Spatial keyword search     Geographic information system     Location-based service    

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A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction Research Article

Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1574-1590 doi: 10.1631/FITEE.2200334

Abstract: Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization. This is to improve the efficiency and accuracy of HS algorithm optimization. Second, the CS operator is introduced to expand the scope of the solution space and improve the density of the population, which can quickly jump out of the local optimum in the randomly generated harmony and update stage. Finally, a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization. Three theorems are proved to reveal HS-CS as a meta-heuristic algorithm. In addition, 12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS. The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness, high convergence speed, and high convergence accuracy. For further verification, HS-CS is used to optimize the back propagation neural network (BPNN) to extract weighted fuzzy production rules. Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules. Therefore, the proposed HS-CS is proved to be effective.

Keywords： Harmony search algorithm     Cuckoo search algorithm     Global convergence     Function optimization     Weighted fuzzy production rule extraction    

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Efficient keyword search over graph-structured data based on minimal covered Article

Asieh GHANBARPOUR, Khashayar NIKNAFS, Hassan NADERI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 448-464 doi: 10.1631/FITEE.1800133

Abstract: Keyword search is an alternative for structured languages in querying graph-structured data. A result to a keyword query is a connected structure covering all or part of the queried keywords. The textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword query. Many previous works examined these properties after retrieving all of the candidate results using a ranking function in a comparative manner. However, this needs a time-consuming search process, which is not appropriate for an interactive system in which the user expects results in the least possible time. This problem has been addressed in recent works by confining the shape of results to examine their coverage and compactness during the search. However, these methods still suffer from the existence of redundant nodes in the retrieved results. In this paper, we introduce the semantic of minimal covered r-clique (MCCr) for the results of a keyword query as an extended model of existing definitions. We propose some efficient algorithms to detect the MCCrs of a given query. These algorithms can retrieve a comprehensive set of non-duplicate MCCrs in response to a keyword query. In addition, these algorithms can be executed in a distributive manner, which makes them outstanding in the field of keyword search. We also propose the approximate versions of these algorithms to retrieve the top-k approximate MCCrs in a polynomial delay. It is proved that the approximate algorithms can retrieve results in two-approximation. Extensive experiments on two real-world datasets confirm the efficiency and effectiveness of the proposed algorithms.

Keywords： Keyword search     Graph mining     Information retrieval     Database     Clique    

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Tabu search based resource allocation in radiological examination process execution None

Chun-hua HE

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 446-458 doi: 10.1631/FITEE.1601802

Abstract: Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a Tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.

Keywords： Radiological examination process (REP)     Resource scheduling and allocation     Tabu search    

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A New Sufficient Conditions and Hamiltonian graphs

Zhao Kewen

Strategic Study of CAE 2003, Volume 5, Issue 11,   Pages 61-64

Abstract:

Let G be a simple graph, δ and a be minimum degree and independence number of G, respectively, Faudree et al showed, in 1991, the Hamiltonian result with condition | N(x)∪ N(y) | ≥n-8. In 1993, Chen further considered the Hamiltonian with condition max |d{x) , d(y)| n/2 for each pair of non-adjacent vertices x , y with 1≤|N(x)∩NV(y)|≤a-l. In this paper a sufficient condition for a graph to be Hamiltonian graph is shown and the following result is obtained ： let G be a 2-connected graph of order n , if| N(x) U N(y) |≥ n-δ-1 for each pair of non-adjacent vertices x, y with 1≤ | N(x)∩ N(y) |α-1, then G is Hamiltonian or G∈{K(n-1)/2, (n + 1)/2, K2* V3K(n-2)/3}，This result generalizes some results in Hamiltonian graphs .

Keywords： Hamiltonian graph     neighborhood union conditions     minimum degree     independence number    

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Constructing pairing-free certificateless public key encryption with keyword search Research Articles

Yang LU, Ji-guo LI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1049-1060 doi: 10.1631/FITEE.1700534

Abstract: Searchable public key encryption enables a storage server to retrieve the publicly encrypted data without revealing the original data contents. It offers a perfect cryptographic solution to encrypted data retrieval in encrypted data storage systems. Certificateless cryptography (CLC) is a novel cryptographic primitive that has many merits. It overcomes the key escrow problem in identity-based cryptosystems and the cumbersome certificate problem in conventional public key cryptosystems. Motivated by the appealing features of CLC, three certificateless encryption with keyword search (CLEKS) schemes were presented in the literature. However, all of them were constructed with the costly bilinear pairing and thus are not suitable for the devices that have limited computing resources and battery power. So, it is interesting and worthwhile to design a CLEKS scheme without using bilinear pairing. In this study, we put forward a pairing-free CLEKS scheme that does not exploit bilinear pairing. We strictly prove that the scheme achieves keyword ciphertext indistinguishability against adaptive chosen-keyword attacks under the complexity assumption of the computational Diffie-Hellman problem in the random oracle model. Efficiency comparison and the simulation show that it enjoys better performance than the previous pairing-based CLEKS schemes. In addition, we briefly introduce three extensions of the proposed CLEKS scheme.

Keywords： Searchable public key encryption     Certificateless public key encryption with keyword search     Bilinear pairing     Computational Diffie-Hellman problem    

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MULKASE: a novel approach for key-aggregate searchable encryption formulti-owner data Regular Papers

Mukti PADHYA, Devesh C. JINWALA

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1717-1748 doi: 10.1631/FITEE.1800192

Abstract: Recent attempts at key-aggregate searchable encryption (KASE) combine the advantages of searching encrypted data with support for data owners to share an aggregate searchable key with a user delegating search rights to a set of data. A user, in turn, is required to submit only one single aggregate trapdoor to the cloud to perform a keyword search across the shared set of data. However, the existing KASE methods do not support searching through data that are shared by multiple owners using a single aggregate trapdoor. Therefore, we propose a MULKASE method that allows a user to search across different data records owned by multiple users using a single trapdoor. In MULKASE, the size of the aggregate key is independent of the number of documents held by a data owner. The size of an aggregate key remains constant even though the number of outsourced ciphertexts goes beyond the predefined limit. Security analysis proves that MULKASE is secure against chosen message attacks and chosen keyword attacks. In addition, the security analysis confirms that MULKASE is secure against cross-pairing attacks and provides query privacy. Theoretical and empirical analyses show that MULKASE performs better than the existing KASE methods. We also illustrate how MULKASE can carry out federated searches.

Keywords： Searchable encryption     Cloud storage     Key-aggregate encryption     Data sharing    

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A new focused crawler using an improved tabu search algorithm incorporating ontology and host information Research Article

Jingfa LIU, Zhen WANG, Guo ZHONG, Zhihe YANG,1007427607@qq.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 6,   Pages 859-875 doi: 10.1631/FITEE.2200315

Abstract: To solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in traditional focused crawling methods, in this paper, we propose a novel using an improved with domain and (FCITS_OH), where a domain is constructed by formal concept analysis to describe topics at the semantic and knowledge levels. To avoid crawling visited hyperlinks and expand the search range, we present an improved tabu search (ITS) algorithm and the strategy of memory. In addition, a comprehensive method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks. Experimental results on both tourism and rainstorm disaster domains show that the proposed s overmatch the traditional s for different performance metrics.

Keywords： Focused crawler     Tabu search algorithm     Ontology     Host information     Priority evaluation    

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Discovering optimal features using static analysis and a genetic search based method for Android malware detection None

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 712-736 doi: 10.1631/FITEE.1601491

Abstract: Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.

Keywords： Genetic algorithm     Static analysis     Android     Malware     Machine learning    

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MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization Research Article

Kai MENG, Chen CHEN, Bin XIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1828-1847 doi: 10.1631/FITEE.2200237

Abstract: The (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal . Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an is designed to accommodate an adequate balance between exploration and exploitation. Finally, a is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering . The results demonstrate the superiority of the MSSSA in addressing practical problems.

Keywords： Swarm intelligence     Sparrow search algorithm     Adaptive parameter control strategy     Hybrid disturbance mechanism     Optimization problems    

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Finding misplaced items using amobile robot in a smart home environment Research Articles

Qi WANG, Zhen FAN, Wei-hua SHENG, Sen-lin ZHANG, Mei-qin LIU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1036-1048 doi: 10.1631/FITEE.1800275

Abstract: Smart homes can provide complementary information to assist home service robots. We present a robotic misplaced item finding (MIF) system, which uses human historical trajectory data obtained in a smart home environment. First, a multi-sensor fusion method is developed to localize and track a resident. Second, a path-planning method is developed to generate the robot movement plan, which considers the knowledge of the human historical trajectory. Third, a real-time object detector based on a convolutional neural network is applied to detect the misplaced item. We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.

Keywords： Home service robot     Smart home     Heterogeneous sensors     Autonomous robot retrieval    

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Vascular segmentation of neuroimages based on a prior shape and local statistics Research Articles

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1099-1108 doi: 10.1631/FITEE.1800129

Abstract: Fast and accurate extraction of vascular structures from medical images is fundamental for many clinical procedures. However, most of the vessel segmentation techniques ignore the existence of the isolated and redundant points in the segmentation results. In this study, we propose a vascular segmentation method based on a prior shape and local statistics. It could efficiently eliminate outliers and accurately segment thick and thin vessels. First, an improved vesselness filter is defined. This quantifies the likelihood of each voxel belonging to a bright tubular-shaped structure. A matching and connection process is then performed to obtain a blood-vessel mask. Finally, the region-growing method based on local statistics is implemented on the vessel mask to obtain the whole vascular tree without outliers. Experiments and comparisons with Frangi’s and Yang’s models on real magneticresonance-angiography images demonstrate that the proposed method can remove outliers while preserving the connectivity of vessel branches.

Keywords： Vesselness filter     Neighborhood     Blood-vessel segmentation     Outlier    

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Vector quantization: a review Regular Papers

Ze-bin WU, Jun-qing YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 507-524 doi: 10.1631/FITEE.1700833

Abstract:

Vector quantization (VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types, tree-structured VQ, direct sum VQ, Cartesian product VQ, lattice VQ, classified VQ, feedback VQ, and fuzzy VQ, according to their codebook generation procedures. Over the past decade, quantization-based approximate nearest neighbor (ANN) search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However, these methods are just finding a balance among speed, accuracy, and memory consumption for ANN search, and sometimes one of these three suffers. So, finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory, is still a problem requiring study.

Keywords： Approximate nearest neighbor search     Image coding     Vector quantization    

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Efficientmesh denoising via robust normal filtering and alternate vertex updating Article

Tao LI, Jun WANG, Hao LIU, Li-gang LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1828-1842 doi: 10.1631/FITEE.1601229

Abstract: The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, we give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging rolling guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with few triangles flipped.

Keywords： Mesh denoising     Guided normal filtering     Alternate vertex updating     Corner-aware neighborhoods    

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Title Author Date Type Operation

Firefly algorithm with division of roles for complex optimal scheduling

Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye,zhaojia925@163.com,chen_9731@163.com,rbxiao@hust.edu.cn,yejun68@sina.com

Journal Article

Finding map regions with high density of query keywords

Zhi YU, Can WANG, Jia-jun BU, Xia HU, Zhe WANG, Jia-he JIN

Journal Article

A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction

Shaoqiang YE, Kaiqing ZHOU, Azlan Mohd ZAIN, Fangling WANG, Yusliza YUSOFF

Journal Article

Efficient keyword search over graph-structured data based on minimal covered

Asieh GHANBARPOUR, Khashayar NIKNAFS, Hassan NADERI

Journal Article

Tabu search based resource allocation in radiological examination process execution

Chun-hua HE

Journal Article

A New Sufficient Conditions and Hamiltonian graphs

Zhao Kewen

Journal Article

Constructing pairing-free certificateless public key encryption with keyword search

Yang LU, Ji-guo LI

Journal Article

MULKASE: a novel approach for key-aggregate searchable encryption formulti-owner data

Mukti PADHYA, Devesh C. JINWALA

Journal Article

A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

Jingfa LIU, Zhen WANG, Guo ZHONG, Zhihe YANG,1007427607@qq.com

Journal Article

Discovering optimal features using static analysis and a genetic search based method for Android malware detection

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK

Journal Article

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG, Chen CHEN, Bin XIN

Journal Article

Finding misplaced items using amobile robot in a smart home environment

Qi WANG, Zhen FAN, Wei-hua SHENG, Sen-lin ZHANG, Mei-qin LIU

Journal Article

Vascular segmentation of neuroimages based on a prior shape and local statistics

Yun TIAN, Zi-feng LIU, Shi-feng ZHAO

Journal Article

Vector quantization: a review

Ze-bin WU, Jun-qing YU

Journal Article

Efficientmesh denoising via robust normal filtering and alternate vertex updating

Tao LI, Jun WANG, Hao LIU, Li-gang LIU

Journal Article