Yang Guan, S. Li, Jingliang Duan, Jie Li, Yangang Ren, Qi Sun, B. Cheng
{"title":"Cover: International Journal of Intelligent Systems, Volume 36 Issue 8 August 2021","authors":"Yang Guan, S. Li, Jingliang Duan, Jie Li, Yangang Ren, Qi Sun, B. Cheng","doi":"10.1002/int.22574","DOIUrl":"https://doi.org/10.1002/int.22574","url":null,"abstract":"","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"58 1","pages":"i"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84584268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.2991/IJCIS.D.210607.001
L. Zedam, B. Baets
Recently, we have introduced six types of composition of ternary fuzzy relations. These compositions are close in spirit to the composition of binary fuzzy relations. Based on these types of composition, we have introduced several types of transitivity of a ternary fuzzy relation and investigated their basic properties. In this paper, we prove additional properties and characterizations of these types of transitivity of a ternary fuzzy relation. Also, we provide a representation theorem for ternary fuzzy relations satisfying these types of transitivity. Finally, we focus on the problem of closing a ternary fuzzy relation with respect to the proposed types of transitivity.
{"title":"Transitive Closures of Ternary Fuzzy Relations","authors":"L. Zedam, B. Baets","doi":"10.2991/IJCIS.D.210607.001","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210607.001","url":null,"abstract":"Recently, we have introduced six types of composition of ternary fuzzy relations. These compositions are close in spirit to the composition of binary fuzzy relations. Based on these types of composition, we have introduced several types of transitivity of a ternary fuzzy relation and investigated their basic properties. In this paper, we prove additional properties and characterizations of these types of transitivity of a ternary fuzzy relation. Also, we provide a representation theorem for ternary fuzzy relations satisfying these types of transitivity. Finally, we focus on the problem of closing a ternary fuzzy relation with respect to the proposed types of transitivity.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"10 1","pages":"1784-1795"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76016435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.2991/IJCIS.D.210609.001
E. S. V. Kumar, S. Balamurugan, S. Sasikala
In present decade, many Educational Institutions use classification techniques and Data mining concepts for evaluating student records. Student Evaluation and classification is very much important for improving the result percentage. Hence, Educational Data Mining based models for analyzing the academic performances have become an interesting research domain in current scenario. With that note, this paper develops a model called Multi-Tier Student Performance Evaluation Model (MTSPEM) using single and ensemble classifiers. The student data from higher educational institutions are obtained and evaluated in this model based on significant factors that impacts greatermanner in student’s performances and results. Further, data preprocessing is carried out for removing the duplicate and redundant data, thereby, enhancing the results accuracy. The multi-tier model contains two phases of classifications, namely, primary classification and secondary classification. The First-Tier classification phase uses Naive Bayes Classification, whereas the second-tier classification comprises the Ensemble classifiers such as Boosting, Stacking and RandomForest (RF). The performance analysis of the proposed work is established for calculating the classification accuracy and comparative evaluations are also performed for evidencing the efficiency of the proposed model.
{"title":"Multi-Tier Student Performance Evaluation Model (MTSPEM) with Integrated Classification Techniques for Educational Decision Making","authors":"E. S. V. Kumar, S. Balamurugan, S. Sasikala","doi":"10.2991/IJCIS.D.210609.001","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210609.001","url":null,"abstract":"In present decade, many Educational Institutions use classification techniques and Data mining concepts for evaluating student records. Student Evaluation and classification is very much important for improving the result percentage. Hence, Educational Data Mining based models for analyzing the academic performances have become an interesting research domain in current scenario. With that note, this paper develops a model called Multi-Tier Student Performance Evaluation Model (MTSPEM) using single and ensemble classifiers. The student data from higher educational institutions are obtained and evaluated in this model based on significant factors that impacts greatermanner in student’s performances and results. Further, data preprocessing is carried out for removing the duplicate and redundant data, thereby, enhancing the results accuracy. The multi-tier model contains two phases of classifications, namely, primary classification and secondary classification. The First-Tier classification phase uses Naive Bayes Classification, whereas the second-tier classification comprises the Ensemble classifiers such as Boosting, Stacking and RandomForest (RF). The performance analysis of the proposed work is established for calculating the classification accuracy and comparative evaluations are also performed for evidencing the efficiency of the proposed model.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"44 1","pages":"1796-1808"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85145561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.2991/IJCIS.D.210601.001
D. K. Bebarta, T. K. Das, C. L. Chowdhary, Xiao Gao
An accurate prediction of future stockmarket trends is a bit challenging as it requires a profound understanding of stock technical indicators, including market-dominant factors and inherent process mechanism. However, the significance of better trading decisions for a successful trader inspires researchers to conceptualize superior model employing the novel set of techniques. In light of this, an intelligent stock trading system utilizing dynamic time windows with case-based reasoning (CBR), and recurrent function link artificial neural network (FLANN) optimizedwith Firefly algorithm is designed. The idea of usingCBRmodule is to offer a dynamic window search to assist the recurrent FLANN architecture for superior fine-tuning the trading operations. This integrated stock trading system is intended to pick the buy/sell window of target stock tomaximize the profit. To demonstrate the applicability of the projected system, the time-series stock data from IBM, Oracle and in currency Euro to INR and USD to INR exchange data on daily closing stock prices are used for simulation. The performance of the proposed model is assessed using error measures such as mean absolute error and mean absolute percent error. Furthermore, the experimental results obtained with/without using CBR is exhibited for different stock and Forex trading data.
{"title":"An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning","authors":"D. K. Bebarta, T. K. Das, C. L. Chowdhary, Xiao Gao","doi":"10.2991/IJCIS.D.210601.001","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210601.001","url":null,"abstract":"An accurate prediction of future stockmarket trends is a bit challenging as it requires a profound understanding of stock technical indicators, including market-dominant factors and inherent process mechanism. However, the significance of better trading decisions for a successful trader inspires researchers to conceptualize superior model employing the novel set of techniques. In light of this, an intelligent stock trading system utilizing dynamic time windows with case-based reasoning (CBR), and recurrent function link artificial neural network (FLANN) optimizedwith Firefly algorithm is designed. The idea of usingCBRmodule is to offer a dynamic window search to assist the recurrent FLANN architecture for superior fine-tuning the trading operations. This integrated stock trading system is intended to pick the buy/sell window of target stock tomaximize the profit. To demonstrate the applicability of the projected system, the time-series stock data from IBM, Oracle and in currency Euro to INR and USD to INR exchange data on daily closing stock prices are used for simulation. The performance of the proposed model is assessed using error measures such as mean absolute error and mean absolute percent error. Furthermore, the experimental results obtained with/without using CBR is exhibited for different stock and Forex trading data.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"3 1","pages":"1763-1772"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91104810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-01DOI: 10.2991/ijcis.d.210520.001
Luping Liu, Wensheng Jia
In this paper, we study the value function with regret minimization algorithm for solving the Nash equilibrium of multi-agent stochastic game (MASG). To begin with, the idea of regret minimization is introduced to the value function, and the value functionwith regretminimization algorithm is designed. Furthermore, we analyze the effect of discount factor to the expected payoff. Finally, the single-agent stochastic game and spatial prisoner’s dilemma (SDP) are investigated in order to support the theoretical results. The simulation results show that when the temptation parameter is small, the cooperation strategy is dominant; when the temptation parameter is large, the defection strategy is dominant. Therefore, we improve the level of cooperation between agents by setting appropriate temptation parameters.
{"title":"The Value Function with Regret Minimization Algorithm for Solving the Nash Equilibrium of Multi-Agent Stochastic Game","authors":"Luping Liu, Wensheng Jia","doi":"10.2991/ijcis.d.210520.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210520.001","url":null,"abstract":"In this paper, we study the value function with regret minimization algorithm for solving the Nash equilibrium of multi-agent stochastic game (MASG). To begin with, the idea of regret minimization is introduced to the value function, and the value functionwith regretminimization algorithm is designed. Furthermore, we analyze the effect of discount factor to the expected payoff. Finally, the single-agent stochastic game and spatial prisoner’s dilemma (SDP) are investigated in order to support the theoretical results. The simulation results show that when the temptation parameter is small, the cooperation strategy is dominant; when the temptation parameter is large, the defection strategy is dominant. Therefore, we improve the level of cooperation between agents by setting appropriate temptation parameters.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"25 1","pages":"1633-1641"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74044478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.2991/IJCIS.D.210409.002
S. Prabin, M. S. Thanabal
Predicting the stock price movements based on quantitative market data modeling is an open problem ever. In stock price prediction, simultaneous achievement of higher accuracy and the fastest prediction becomes a challenging problem due to the hidden information found in raw data. Various prediction models based on machine learning algorithms have been proposed in the literature. In general, these models start with the training phase followed by the testing phase. In the training phase, the past stock market data are used to learn the patterns toward building a model that would then use to predict future stock prices. The performance of such learning algorithms heavily depends on the quality of the data as well as optimal learning parameters. Among the conventional prediction methods, the use of neural network has greatest research interest because of their advantages of self-organizing, distributed processing, and self-learning behaviors. In this work, dynamic nature of the data is mainly focused. In conventional models the retraining has to be carried out for two cases: the data used for training has higher noise and outliers or model trained without preprocessing; the learned data has to update dynamically for recent changes. In this sense, propose a self-repairing dynamic model called repairing artificial neural network (RANN) that correct such errors effectively. The repairing includes adjusting the prediction model from noise, outliers, removing a data sample, and adjusting an attribute value. Hence, the total reconstruction of the prediction model could be avoided while saving training time. The proposed model is validated with five different real-time stock market data and the results are quantified to analyze its performance.
{"title":"A Repairing Artificial Neural Network Model-Based Stock Price Prediction","authors":"S. Prabin, M. S. Thanabal","doi":"10.2991/IJCIS.D.210409.002","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210409.002","url":null,"abstract":"Predicting the stock price movements based on quantitative market data modeling is an open problem ever. In stock price prediction, simultaneous achievement of higher accuracy and the fastest prediction becomes a challenging problem due to the hidden information found in raw data. Various prediction models based on machine learning algorithms have been proposed in the literature. In general, these models start with the training phase followed by the testing phase. In the training phase, the past stock market data are used to learn the patterns toward building a model that would then use to predict future stock prices. The performance of such learning algorithms heavily depends on the quality of the data as well as optimal learning parameters. Among the conventional prediction methods, the use of neural network has greatest research interest because of their advantages of self-organizing, distributed processing, and self-learning behaviors. In this work, dynamic nature of the data is mainly focused. In conventional models the retraining has to be carried out for two cases: the data used for training has higher noise and outliers or model trained without preprocessing; the learned data has to update dynamically for recent changes. In this sense, propose a self-repairing dynamic model called repairing artificial neural network (RANN) that correct such errors effectively. The repairing includes adjusting the prediction model from noise, outliers, removing a data sample, and adjusting an attribute value. Hence, the total reconstruction of the prediction model could be avoided while saving training time. The proposed model is validated with five different real-time stock market data and the results are quantified to analyze its performance.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"32 1","pages":"1337-1355"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85555422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.2991/IJCIS.D.210329.002
Gezahagne Mulat Addis, N. Kausar, M. Munir, Y. Chu
In group theory, the commutator is a binary operation on the lattice of normal subgroups of a group which has an important role in the study of solvable, Abelian and nilpotent groups. Given normal subgroups A and B of a group H, their commutator [A, B] is defined to be the smallest normal subgroup of H containing all elements of the form a−1b−1ab for a ∈ A and b ∈ B. In other words, [A,B] is the largest normal subgroup K ofH such that in the quotient group H∕K every element of A∕K commutes with every element of B∕K. Thus we have a binary operation in the lattice of normal subgroups. This binary operation, together with the lattice operations, carries much of the information about how a group is put together. The operation is also interesting in its own right. It is a commutative, monotone operation, completely distributive with respect to joins in the lattice.
{"title":"The Commutator of Fuzzy Congruences in Universal Algebras","authors":"Gezahagne Mulat Addis, N. Kausar, M. Munir, Y. Chu","doi":"10.2991/IJCIS.D.210329.002","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210329.002","url":null,"abstract":"In group theory, the commutator is a binary operation on the lattice of normal subgroups of a group which has an important role in the study of solvable, Abelian and nilpotent groups. Given normal subgroups A and B of a group H, their commutator [A, B] is defined to be the smallest normal subgroup of H containing all elements of the form a−1b−1ab for a ∈ A and b ∈ B. In other words, [A,B] is the largest normal subgroup K ofH such that in the quotient group H∕K every element of A∕K commutes with every element of B∕K. Thus we have a binary operation in the lattice of normal subgroups. This binary operation, together with the lattice operations, carries much of the information about how a group is put together. The operation is also interesting in its own right. It is a commutative, monotone operation, completely distributive with respect to joins in the lattice.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"28 1","pages":"1322-1336"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79378889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.2991/IJCIS.D.210409.001
Muhammad Bilal Khan, M. Noor, L. Abdullah, Y. Chu
It is well known that convexity and nonconvexity develop a strong relationship with different types of integral inequalities. Due to the importance of the concept of nonconvexity and integral inequality, in this paper, we present some new classes of preinvex fuzzy-interval-valued functions involving two arbitrary auxiliary functions is known as ( h1,h2 ) -preinvex fuzzy-interval-valued functions ( ( h1,h2 ) -preinvex fuzzy-IVFs). With the help of these classes, we derive some new Hermite–Hadamard inequalities (HH-inequalities) by means of fuzzy order relation on fuzzy-interval space and verify with the support of some nontrivial examples. This fuzzy order relation is defined level-wise through Kulisch–Miranker order relation defined on fuzzy-interval space. Moreover, several new and previously known results are also discussed which can be deducted from our main results. These results and different approaches may open new directions for fuzzy optimization problems, modeling, and interval-valued functions.
{"title":"Some New Classes of Preinvex Fuzzy-Interval-Valued Functions and Inequalities","authors":"Muhammad Bilal Khan, M. Noor, L. Abdullah, Y. Chu","doi":"10.2991/IJCIS.D.210409.001","DOIUrl":"https://doi.org/10.2991/IJCIS.D.210409.001","url":null,"abstract":"It is well known that convexity and nonconvexity develop a strong relationship with different types of integral inequalities. Due to the importance of the concept of nonconvexity and integral inequality, in this paper, we present some new classes of preinvex fuzzy-interval-valued functions involving two arbitrary auxiliary functions is known as ( h1,h2 ) -preinvex fuzzy-interval-valued functions ( ( h1,h2 ) -preinvex fuzzy-IVFs). With the help of these classes, we derive some new Hermite–Hadamard inequalities (HH-inequalities) by means of fuzzy order relation on fuzzy-interval space and verify with the support of some nontrivial examples. This fuzzy order relation is defined level-wise through Kulisch–Miranker order relation defined on fuzzy-interval space. Moreover, several new and previously known results are also discussed which can be deducted from our main results. These results and different approaches may open new directions for fuzzy optimization problems, modeling, and interval-valued functions.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"10 1","pages":"1403-1418"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81911515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.2991/ijcis.d.210222.001
L. Abdullah, Zheeching Ong, Siti Nuraini Rahim
The decision-making trial and evaluation laboratory (DEMATEL) method has been applied to solve numerous multi-criteria decision-making (MCDM) problems where crisp numbers are utilized in defining linguistic evaluation. Previous literature suggests that the intuitionistic fuzzy DEMATEL (IF-DEMATEL) can offer a new decision-making method in solving MCDM problems where intuitionistic fuzzy sets (IFSs) are utilized in defining linguistic evaluation. This paper aims to develop a cause–effect diagram of subcontractor selection using a modified IF-DEMATEL method. In this paper, three modifications are made to the IF-DEMATEL method. Two memberships of IFSs, relative weights of experts, and a transformation equation are the elements introduced to the IF-DEMATEL. The linguistic variables that defined in IFSs are meant to capture wide arrays of uncertain and fuzzy information in solving MCDM problems. Furthermore, the modified IF-DEMATEL is applied it to a subcontractors’ selection problem where groups of cause and effect criteria are segregated. A group of experts’ opinions were sought to provide linguistic evaluations regarding the degree of influence between criteria in subcontractors’ selection. The results show that four criteria are identified as cause criteria while six other criteria are identified as effect criteria. The results also suggest that the criteria “experience” is the main cause that influence the selection of subcontractors. The identification of cause and effect criteria would be a great significance for practical implementation of subcontractors’ selection.
{"title":"An Intuitionistic Fuzzy Decision-Making for Developing Cause and Effect Criteria of Subcontractors Selection","authors":"L. Abdullah, Zheeching Ong, Siti Nuraini Rahim","doi":"10.2991/ijcis.d.210222.001","DOIUrl":"https://doi.org/10.2991/ijcis.d.210222.001","url":null,"abstract":"The decision-making trial and evaluation laboratory (DEMATEL) method has been applied to solve numerous multi-criteria decision-making (MCDM) problems where crisp numbers are utilized in defining linguistic evaluation. Previous literature suggests that the intuitionistic fuzzy DEMATEL (IF-DEMATEL) can offer a new decision-making method in solving MCDM problems where intuitionistic fuzzy sets (IFSs) are utilized in defining linguistic evaluation. This paper aims to develop a cause–effect diagram of subcontractor selection using a modified IF-DEMATEL method. In this paper, three modifications are made to the IF-DEMATEL method. Two memberships of IFSs, relative weights of experts, and a transformation equation are the elements introduced to the IF-DEMATEL. The linguistic variables that defined in IFSs are meant to capture wide arrays of uncertain and fuzzy information in solving MCDM problems. Furthermore, the modified IF-DEMATEL is applied it to a subcontractors’ selection problem where groups of cause and effect criteria are segregated. A group of experts’ opinions were sought to provide linguistic evaluations regarding the degree of influence between criteria in subcontractors’ selection. The results show that four criteria are identified as cause criteria while six other criteria are identified as effect criteria. The results also suggest that the criteria “experience” is the main cause that influence the selection of subcontractors. The identification of cause and effect criteria would be a great significance for practical implementation of subcontractors’ selection.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"5 1","pages":"991-1002"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80081101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-01DOI: 10.2991/ijcis.d.210203.008
M. El-Shorbagy, A. Ayoub
This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. In addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies; where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem.
{"title":"Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems","authors":"M. El-Shorbagy, A. Ayoub","doi":"10.2991/ijcis.d.210203.008","DOIUrl":"https://doi.org/10.2991/ijcis.d.210203.008","url":null,"abstract":"This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. In addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies; where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem.","PeriodicalId":13602,"journal":{"name":"Int. J. Comput. Intell. Syst.","volume":"24 1","pages":"783-793"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78137036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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