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Sensor Localization in Wireless Sensor Networks Using cultural algorithm

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020年第4期11卷 106-122页

作者： Kulkarni, Vaishali Raghavendra Desai, Veena MS Ramaiah Univ Appl Sci Bangalore Karnataka India KLS Gogte Inst Technol Belgaum Karnataka India

Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.

关键词： Artificial Bee Colony algorithm cultural algorithm Localization Particle Swarm Optimization Wireless Sensor Networks

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A Novel Multi-objective cultural algorithm Embedding Five-Element Cycle Optimization

A Novel Multi-objective Cultural Algorithm Embedding Five-El...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mao, Zhengyan Xiang, Yue Zhang, Yijie Liu, Mandan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728169293

The cultural algorithm, as a dual-inheritance framework designed for optimization problems, can incorporate any population-adopted evolutionary computation technique in its population space. On the other hand, based on the Five-Elements Cycle Model derived from the ancient Chinese Five Elements (metal, wood, water, fire, earth) theory, the five-elements cycle optimization algorithm was proved to be effective in solving continuous function optimization problems. In this work, we propose a multi-objective cultural algorithm with a five-elements-cycle-optimization-based population space, where the five-element cycle model is adopted as the evolution scheme in the population space of the cultural algorithm framework. Simulation results on 12 classic benchmark problems show that the proposed algorithm can effectively solve continuous optimization functions and obtains satisfactory non-dominated solutions compared with 8 representative multi-objective algorithms.

关键词： multi-objective evolutionary optimization cultural algorithm five-elements cycle model

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Multimodal fake news detection using a cultural algorithm with situational and normative knowledge

Multimodal fake news detection using a Cultural Algorithm wi...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Shah, Priyanshi Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9781728169293

The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. A fake news item is usually created by manipulating photos, text, or videos that indicate the need for multimodal detection. Researchers are building detection algorithms with an aim for high accuracy as this will have a massive impact on the prevailing social and political issues. A shortcoming of existing strategies for identifying fake news is their inability to learn a feature representation of multimodal (textual+visual) information. In this paper, we present a novel approach using a cultural algorithm with situational and normative knowledge to detect fake news using both text and images. An extensive set of experiments have been carried out on real-world multimedia datasets collected from Weibo and Twitter. The proposed method outperforms the state-of-the-art methods for identifying fake news in terms of accuracy by 9% on average.

关键词： Fake news detection Sentiment analysis Segmentation process cultural algorithm

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cultural algorithm Initializes Weights of Neural Network Model for Annual Electricity Consumption Prediction

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第6期11卷 101-110页

作者： Phatai, Gawalee Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen Thailand

The accurate prediction of annual electricity consumption is crucial in managing energy operations. The neural network (NN) has achieved a lot of achievements in yearly electricity consumption prediction due to its universal approximation property. However, the well-known back-propagation (BP) algorithms for training NN has easily got stuck in local optima. In this paper, we study the weights initialization of NN for the prediction of annual electricity consumption using the cultural algorithm (CA), and the proposed algorithm is named as NN-CA. The NN-CA was compared to the weights initialization using the other six metaheuristic algorithms as well as the BP. The experiments were conducted on the annual electricity consumption datasets taken from 21 countries. The experimental results showed that the proposed NN-CA achieved more productive and better prediction accuracy than other competitors. This result indicates the possible consequences of the proposed NN-CA in the application of annual electricity consumption prediction.

关键词： Neural network weights initialization metaheuristic algorithm cultural algorithm annual electricity consumption prediction

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Multimodal fake news detection using a cultural algorithm with situational and normative knowledge

Multimodal fake news detection using a Cultural Algorithm wi...

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作者： Priyanshi Virendra Shah University of Windsor

学位级别：硕士

The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. This makes detection of fake news an extremely important challenge. A fake news item is usually created by manipulating photos, text or videos that indicate the need for multimodal detection. Researchers are building detection algorithms with the aim of high accuracy as this will have a massive impact on the prevailing social and political issues. A shortcoming of existing strategies for identifying fake news is their inability to learn a feature representation of multimodal (textual+visual) information. In this thesis research, we present a novel approach using a cultural algorithm with situational and normative knowledge to detect fake news using both text and images. The proposed model's principal innovation is to use the power of natural language processing like sentiment analysis, segmentation process for feature extraction, and optimizing it with a cultural algorithm. Then the representations from both modalities are fused, which is ﬁnally used for classiﬁcation. An extensive set of experiments is carried out on real-world multimedia datasets collected from Weibo and Twitter. The proposed method outperforms the state-of-the-art methods for identifying fake news.

关键词： cultural algorithm fake news detection Segmentation process Sentiment analysis

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An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 781-801页

作者： Jafari, Malihe Salajegheh, Eysa Salajegheh, Javad Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration-exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature.

关键词： Elephant herding optimization cultural algorithm Hybrid metaheuristic algorithms Mathematical optimization problems Truss weight minimization problems

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An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm

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JOURNAL OF SUPERCOMPUTING 2019年第10期75卷 6904-6933页

作者： Mohammadhosseini, Mahdieh Haghighat, Abolfazl Toroghi Mahdipour, Ebrahim Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Islamic Azad Univ Qazvin Branch Dept Comp & Informat Technol Engn Qazvin Iran

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased.

关键词： Cloud computing Virtual machine placement Balance vector Cloud data centers Energy consumption cultural algorithm

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A hybrid elephant herding optimization and cultural algorithm for energy-balanced cluster head selection scheme to extend the lifetime in WSNs

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第15期33卷 e4538-e4538页

作者： Murugadass, Gopal Sivakumar, Poruran Anna Univ Dept Informat & Commun Engn Chennai Tamil Nadu India Dr NGP Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Clustering-based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO-CA-OCHS) scheme is proposed to extend the lifetime. This proposed HEHO-CA-OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO-CA-OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO-CA-OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

关键词： clan updating operator cultural algorithm elephant herding optimization optimal cluster head selection separating operator

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A cultural algorithm for Determining Similarity Values Between Users in Recommender Systems 1

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22nd International Conference on Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Selvarajah, Kalyani Kobti, Ziad Kargar, Mehdi Univ Windsor Sch Comp Sci Windsor ON Canada Ryerson Univ Ted Rogers Sch Informat Technol Management Toronto ON Canada

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

Recommendation systems for online marketing often rely on users' ratings to evaluate the similarity between users in Collaborative Filtering (CF) recommender systems. This paper applies knowledge-based evolutionary optimization algorithms called cultural algorithms (CA) to evaluate the similarity between users. To deal with the sparsity of data, we combine CF with a trust network between users. The trust network is then clustered using Singular Value Decomposition (SVD) which helps to discover the top neighbors' trust value. By incorporating trust relationships with CF, we predict the rating by each user on a given item. This study uses the Epinions dataset in order to train and test the accuracy of the results of the approach. The results are then compared against those produced by Genetic algorithms (GA), Cosine, and Pearson Correlation Coefficient (PCC) methods. The comparison of the results suggests that the proposed algorithm outperforms the other similarity functions.

关键词： cultural algorithm Recommender system Collaborative Filtering Trust aware

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Solving Dynamic Multi-Objective Optimization Problems Using cultural algorithm based on Decomposition 2019

Solving Dynamic Multi-Objective Optimization Problems Using ...

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Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Ramya Ravichandran Ziad Kobti School of Computer Science University of Windsor Windsor ON Canada

ISBN: (纸本)9781450376259

The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new cultural algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, Multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.

关键词： cultural algorithm Decomposition Dynamic Multi-Objective Optimization

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