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Opinion leader detection using whale optimization algorithm in online social network

EXPERT SYSTEMS WITH APPLICATIONS 2020年 142卷 113016-000页

作者： Jain, Lokesh Katarya, Rahul Sachdeva, Shelly Delhi Technol Univ Dept Comp Sci & Engn New Delhi India Natl Inst Technol Dept Comp Sci & Engn New Delhi India

In the current digital era, optimization is one of the most significant problems in the social network. Most of the issues related to optimization are NP-complete and not possible to solve them in polynomial time. Detection of opinion leader based on their optimized centrality measure is a critical issue. The opinion leaders have a non-trivial influence on the other user's decision-making process and can solve various problems related to the diffusion of new products and innovations in the real world. In this paper, we proposed a new Social Network-based whale optimization algorithm (SNWOA) to find the top-N opinion leaders by measuring the reputation of the user using various standard optimization function in the network. The proposed algorithm is advantageous to determine the opinion leaders because it based on the bubble-net hunting behavior of humpback whales. The algorithm found the best possible solution as the number of users raises progressively in the network;therefore, the general complexity of the algorithm remains unchanged. Besides, we also proposed a new approach to categorize the communities based on the similarity index comprising neighbor similarity and clustering coefficient as the significant components. Initially, we computed the objective function of each user by using their centralities and deployed the proposed algorithm with different optimization functions to identify the local and universal opinion leaders. We implemented the proposed algorithm on the real and synthesized datasets and compared the result based on the accuracy, precision, recall, and F1-score. The result indicates that the proposed algorithms give a better result as compared to the other standard Social Network Analysis (SNA) measures. We also concluded that the community partitioning algorithm is even better than the other community detection algorithms based on different parameters and computational time. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Node centrality Community detection Nature-inspired meta-heuristic algorithm Online social network Opinion leader

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whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

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SN APPLIED SCIENCES 2019年第5期1卷 390页

作者： Shuaib, Maryam Abdulhamid, Shafi'i Muhammad Adebayo, Olawale Surajudeen Osho, Oluwafemi Idris, Ismaila Alhassan, John K. Rana, Nadim Fed Univ Technol Dept Cyber Secur Sci Minna Nigeria Jazan Univ Coll Comp Sci & Informat Syst Jazan Saudi Arabia

Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet *** research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with *** results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.

关键词： whale optimization algorithm Metaheuristic algorithm Email spam Classification algorithms Rotation forest Feature selection

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Risk management in perishable food distribution operations A distribution route selection model and whale optimization algorithm

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2020年第2期120卷 291-311页

作者： Wang, Yingchao Yang, Chen Hou, Hanpo Beijing Technol & Business Univ Sch Business Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China

Purpose The purpose of this paper is to predict or even control the food safety risks during the distribution of perishable foods. Considering the food safety risks, the distribution route of perishable foods is reasonably arranged to further improve the efficiency of cold chain distribution and reduce distribution costs. Design/methodology/approach This paper uses the microbial growth model to identify a food safety risk coefficient to describe the characteristics of food safety risks that increase over time. On this basis, with the goal of minimizing distribution costs, the authors establish a vehicle routing problem with a food safety Risk coefficient and a Time Window (VRPRTW) for perishable foods. Then, the Weight-Parameter whale optimization algorithm (WPWOA) which introduces inertia weight and dynamic parameter into the native whale optimization algorithm is designed for solving this model. Moreover, benchmark functions and numerical simulation are used to test the performance of the WPWOA. Findings Based on numerical simulation, the authors obtained the distribution path of perishable foods under the restriction of food safety risks. Moreover, the WPWOA can significantly outperform other algorithms on most of the benchmark functions, and it is faster and more robust than the native WOA and avoids premature convergence. Originality/value This study indicates that the established model and the algorithm are effective to control the risk of perishable food in distribution process. Besides, it extends the existing literature and can provide a theoretical basis and practical guidance for the vehicle routing problem of perishable foods.

关键词： Risk management Supply chain management Vehicle routing problem whale optimization algorithm

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Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

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SOFT COMPUTING 2020年第17期24卷 13037-13050页

作者： Bhaskar, N. Kumar, P. Mohan Anna Univ Dept IT KCG Coll Technol Chennai Tamil Nadu India Anna Univ Dept CSE Jeppiaar Engn Coll Chennai Tamil Nadu India

Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.

关键词： Optimal query Crowdsource whale optimization algorithm KNN and range query

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An improved whale optimization algorithm with armed force program and strategic adjustment

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APPLIED MATHEMATICAL MODELLING 2020年第0期81卷 603-623页

作者： Jiang, Ruiye Yang, Ming Wang, Songyan Chao, Tao Harbin Inst Technol Control & Simulat Syst Harbin 150001 Heilongjiang Peoples R China

In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Constrained functions Computation intelligence CEC optimization functions Engineering design problems Swarm intelligence whale optimization algorithm

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Dynamic software rejuvenation in web services: a whale optimization algorithm-based approach

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第2期28卷 890-903页

作者： Rezaei Kalantari, Kimia Ebrahimnejad, Ali Motameni, Homayun Islamic Azad Univ Dept Comp Engn Babol Branch Babol Sar Iran Islamic Azad Univ Dept Math Qaemshahr Branch Qaemshahr Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, metaheuristics seem to be suitable. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the whale optimization algorithm. The threshold for the rejuvenation of each of the web services is considered and training is done based on the features of the service providers as well as the needs of the receivers. The whale optimization algorithm with the criterion of movement radius is utilized for flexibility of web service provider selection. Here, we detect and rejuvenate systems that required rejuvenation before the occurrence of a fault. The simulation results reveal that our strategy can decrease the failure rate by an average of 30 percent in comparison with state-of-the-art strategies and improve the system availability in web services.

关键词： Software aging software rejuvenation web service whale optimization algorithm

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A novel discrete whale optimization algorithm for solving knapsack problems

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APPLIED INTELLIGENCE 2020年第10期50卷 3350-3366页

作者： Li, Ya He, Yichao Liu, Xuejing Guo, Xiaohu Li, Zewen Hebei GEO Univ Coll Informat & Engn Shijiazhuang 050031 Hebei Peoples R China

whale optimization algorithm (WOA) is a recently proposed meta-heuristic algorithm which imitates the hunting behavior of humpback whales. Due to its characteristic advantages, it has found its place in the mature population-based methods in many scientific and engineering fields. Because WOA was proposed for continuous optimization, it cannot be directly used to solve discrete optimization problems. For this purpose, we first give a new V -shaped function by drawing lesson from the existing discretization methods, which transfer a real vector to an integer vector. On this basis, we propose a novel discrete whale optimization algorithm (DWOA). DWOA uses the new proposed V -shaped function to generate an integer vector, and it can be used to solve discrete optimization problems with solution space {0,1, horizontal ellipsis ,m(1)}x{0,1, horizontal ellipsis ,m(2)}x horizontal ellipsis x{0,1, horizontal ellipsis ,m(n)}. To verify effectiveness of DWOA for the 0-1 knapsack problem and the discount {0-1} knapsack problem, we solve their benchmark instances from published literature and compare with the state-of-the-art algorithms. The comparison results show that the DWOA has more superiority than existing algorithms for the two kinds of knapsack problems.

关键词： whale optimization algorithm Meta-heuristic algorithm V -shaped function 0-1knapsack problem Discounted {0-1} knapsack problem

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A novel binarization technique based on whale optimization algorithm for better restoration of palm leaf manuscript

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年 1-8页

作者： Alexander, T. Jerry Kumar, S. Suresh Sathyabama Inst Sci & Technol Fac Elect Engn Chennai Tamil Nadu India Swarnandhra Coll Engn & Technol Narasapur India

Palm leaf manuscript is a rich source of information pertaining to culture and heritage from the ancient period which is written on Palm leaves. They share surplus knowledge base in the field of art, medicine, culture, science, astronomy, astrology, crafts, literature, etc. Due to environmental effects and various other factors like handling, storage methods, etc. these manuscripts undergoes degradation. Hence for preservation of the information contained in the palm leaves, digitization of the manuscript is needed. The manuscript is either scanned or photographed for digitization and stored for preserving the content. For storage, enormous storage space is required and in addition, the degradation due to the noise should be removed for restoration of the text in the Palm leaf manuscript. Textual retrieval is done by using binarization method. Many binarization techniques such as Local threshold methods, Global thresholding, Otsu histogram thresholding, Adaptive thresholding techniques are available in the literature. Even then lots of challenges are present due to the degradation of the Palm-leaf manuscript. Swarm intelligence techniques are gaining importance in almost all domains for the optimization of parameters. In this present work, an attempt is made to use whale optimization algorithm for optimization of Adaptive thresholding and the results are compared with the existing techniques. It proves the ascendance of the proposed technique over the prevailing techniques.

关键词： Binarization Palm leaf manuscript Niblack Sauvola Adaptive thresholding whale optimization algorithm Text retrieval Image restoration

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Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第5期17卷 5987-6025页

作者： Zhou, Yongquan Niu, Yanbiao Luo, Qifang Jiang, Ming Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Guangxi Inst Digital Technol Nanning 530000 Peoples R China

This paper presents an improved teaching learning-based whale optimization algorithm (TSWOA) used the simplex method. First of all, the combination of WOA algorithm and teaching learning-based algorithm not only achieves a better balance between exploration and exploitation of WOA, but also makes whales have self-learning ability from the biological background, and greatly enriches the theory of the original WOA algorithm. Secondly, the WOA algorithm adds the simplex method to optimize the current worst unit, averting the agents to search at the boundary, and increasing the convergence accuracy and speed of the algorithm. To evaluate the performance of the improved algorithm, the TSWOA algorithm is employed to train the multi-layer perceptron (MLP) neural network. It is a difficult thing to propose a well-pleasing and valid algorithm to optimize the multi-layer perceptron neural network. Fifteen different data sets were selected from the UCI machine learning knowledge and the statistical results were compared with GOA, GSO, SSO, FPA, GA and WOA, severally. The statistical results display that better performance of TSWOA compared to WOA and several well-established algorithms for training multi-layer perceptron neural networks.

关键词： teaching learning-based whale optimization algorithm multi-layer perceptron (MLP) neural network metaheuristic algorithm

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A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm

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APPLIED INTELLIGENCE 2020年第11期50卷 3808-3821页

作者： Chen, Jiaoliao Zhuo, Xingai Xu, Fang Wang, Jiacai Zhang, Dan Zhang, Libin Zhejiang Univ Technol Minist Educ Key Lab E&M Hangzhou 310012 Zhejiang Peoples R China York Univ Lassonde Sch Engn Dept Mech Engn Toronto ON M2J 4A6 Canada

The least squares support vector machine (LSSVM) is a useful binary classifier, but its performance is limited due to the lack of sparseness. The density-dependent quantized LSSVM (DSM) with quantized input data can increase the sparseness to effectively accomplish binary classification. However, the DSM cannot be directly used in multi-classification applications for most practical data-classification problems. We propose a novel multi-classifier based on a density-dependent quantized binary tree LSSVM (DBSM) and the logistic global whale optimization algorithm (LWA) to improve multi-classification accuracy and computational efficiency. The DBSM consists of multiple DSM classifiers, which hierarchically divide data according to a modified binary tree architecture. The tree architecture is constructed quickly and correctly with the quantized data instead of the original input data. An appropriate initial population of DBSM parameters is generated by using a logistic map and an improved opposition-based learning strategy. Then, the DBSM parameters are optimized by the whale optimization algorithm integrated with the gbest-guided artificial bee colony algorithm. According to the experimental results, the DBSM solves multi-classification problems faster than the one-versus-one based support vector machine (OVO-SVM) and the one-versus-all based LSSVM without sacrificing accuracy. The LWA precisely finds the optimal DBSM parameters without a heavy computational burden, in contrast to recent optimization algorithms. The proposed classifier achieves a 3.39% higher accuracy and consumes 52.83% less time than the genetic algorithm-based OVO-SVM. These results prove that the LWA-DBSM can complete multi-class classification tasks precisely and quickly.

关键词： Multi-class classification Least squares support vector machine whale optimization algorithm Binary tree

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