Pub Date : 2015-10-01DOI: 10.4018/IJNCR.2015100101
G. Klepac
Complex analytical environment is challenging environment for finding customer profiles. In situation where predictive model exists like Bayesian networks challenge became even bigger regarding combinatory explosion. Complex analytical environment can be caused by multiple modality of output variable, fact that each node of Bayesian network can potetnitaly be target variable for profiling, as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of Bayesian network. This paper will show how partical swarm optimization algorithm can be powerfull tool for finding optimal customer profiles given target conditions as evidences within Bayesian networks.
{"title":"Particle Swarm Optimization Algorithm as a Tool for Profile Optimization","authors":"G. Klepac","doi":"10.4018/IJNCR.2015100101","DOIUrl":"https://doi.org/10.4018/IJNCR.2015100101","url":null,"abstract":"Complex analytical environment is challenging environment for finding customer profiles. In situation where predictive model exists like Bayesian networks challenge became even bigger regarding combinatory explosion. Complex analytical environment can be caused by multiple modality of output variable, fact that each node of Bayesian network can potetnitaly be target variable for profiling, as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of Bayesian network. This paper will show how partical swarm optimization algorithm can be powerfull tool for finding optimal customer profiles given target conditions as evidences within Bayesian networks.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127537700","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 : 2015-10-01DOI: 10.4018/IJNCR.2015100102
S. M. Cruz, G. K. Vianna
The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.
{"title":"Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops","authors":"S. M. Cruz, G. K. Vianna","doi":"10.4018/IJNCR.2015100102","DOIUrl":"https://doi.org/10.4018/IJNCR.2015100102","url":null,"abstract":"The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133751450","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 : 2015-07-01DOI: 10.4018/IJNCR.2015070101
B. Arun, T. Kumar
Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization MBO. An MBO based view selection algorithm MBOVSA, which aims to select views that incur the minimum total cost of evaluating all the views TVEC, is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.
{"title":"Materialized View Selection using Marriage in Honey Bees Optimization","authors":"B. Arun, T. Kumar","doi":"10.4018/IJNCR.2015070101","DOIUrl":"https://doi.org/10.4018/IJNCR.2015070101","url":null,"abstract":"Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization MBO. An MBO based view selection algorithm MBOVSA, which aims to select views that incur the minimum total cost of evaluating all the views TVEC, is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027304","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 : 2015-07-01DOI: 10.4018/IJNCR.2015070103
H. Yousefi, Mehdi Fallahnezhad
Needle insertion has been a very popular minimal invasive surgery method in cancer detection, soft tissue properties recognition and many other surgical operations. Its applications were observed in brain biopsy, prostate brachytherapy and many percutaneous therapies. In this study the authors would like to provide a model of needle force in soft tissue insertion. This model has been developed using higher order polynomial networks. In order to provide a predictive model one-dimensional force sensed on enacting end of bevel-tip needles. The speeds of penetration for quasi-static processes have chosen to be in the range of between 5 mm/min and 300 mm/min. Second and third orders of polynomials employed in the network which contains displacement and speed as their main affecting parameters in the simplified model. Results of fitting functions showed a reliable accuracy in force-displacement graph.
{"title":"Multi-Objective Higher Order Polynomial Networks to Model Insertion Force of Bevel-Tip Needles","authors":"H. Yousefi, Mehdi Fallahnezhad","doi":"10.4018/IJNCR.2015070103","DOIUrl":"https://doi.org/10.4018/IJNCR.2015070103","url":null,"abstract":"Needle insertion has been a very popular minimal invasive surgery method in cancer detection, soft tissue properties recognition and many other surgical operations. Its applications were observed in brain biopsy, prostate brachytherapy and many percutaneous therapies. In this study the authors would like to provide a model of needle force in soft tissue insertion. This model has been developed using higher order polynomial networks. In order to provide a predictive model one-dimensional force sensed on enacting end of bevel-tip needles. The speeds of penetration for quasi-static processes have chosen to be in the range of between 5 mm/min and 300 mm/min. Second and third orders of polynomials employed in the network which contains displacement and speed as their main affecting parameters in the simplified model. Results of fitting functions showed a reliable accuracy in force-displacement graph.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133063237","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 : 2015-07-01DOI: 10.4018/IJNCR.2015070102
Santosh Kumar, S. Singh
Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups collective behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization PSO, and Bacterial Foraging Optimization BFO optimization algorithms. The hybrid approach consists of two parts: 1 two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; 2 In proposed hybrid approach, appearance based holistic face representation and recognition approaches such as Principal Component Analysis PCA, Local Discriminant Analysis LDA, Independent Component Analysis ICA and Discrete Cosine Transform DCT extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.
{"title":"Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms","authors":"Santosh Kumar, S. Singh","doi":"10.4018/IJNCR.2015070102","DOIUrl":"https://doi.org/10.4018/IJNCR.2015070102","url":null,"abstract":"Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups collective behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization PSO, and Bacterial Foraging Optimization BFO optimization algorithms. The hybrid approach consists of two parts: 1 two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; 2 In proposed hybrid approach, appearance based holistic face representation and recognition approaches such as Principal Component Analysis PCA, Local Discriminant Analysis LDA, Independent Component Analysis ICA and Discrete Cosine Transform DCT extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"93 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391922","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 : 2015-04-01DOI: 10.4018/ijncr.2015040101
He-sheng Tang, Lijun Xie, S. Xue
This paper introduces a novel swarm intelligence based algorithm named comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. With the new strategy in this variant of particle swarm optimization (PSO), historical best information for all other particles is used to update a particle's velocity. This means that the particles have more exemplars to learn from, as well as have a larger potential space to fly, avoiding premature convergence. Simulation results for identifying the parameters of a five degree-of-freedom (DOF) structural system under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate improved estimation of these parameters by the CLPSO when compared with those obtained from standard PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a twelve-story shear building shaking table model.
{"title":"Usage of Comprehensive Learning Particle Swarm Optimization for Parameter Identification of Structural System","authors":"He-sheng Tang, Lijun Xie, S. Xue","doi":"10.4018/ijncr.2015040101","DOIUrl":"https://doi.org/10.4018/ijncr.2015040101","url":null,"abstract":"This paper introduces a novel swarm intelligence based algorithm named comprehensive learning particle swarm optimization (CLPSO) to identify parameters of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. With the new strategy in this variant of particle swarm optimization (PSO), historical best information for all other particles is used to update a particle's velocity. This means that the particles have more exemplars to learn from, as well as have a larger potential space to fly, avoiding premature convergence. Simulation results for identifying the parameters of a five degree-of-freedom (DOF) structural system under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate improved estimation of these parameters by the CLPSO when compared with those obtained from standard PSO. In addition, the efficiency and applicability of the proposed method are experimentally examined by a twelve-story shear building shaking table model.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134358951","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 : 2015-04-01DOI: 10.4018/ijncr.2015040102
Marcos A. Schreiner, M. Castilho, Fabiano Silva, Luis Allan Künzle, R. Montaño
In this paper the classical planning problem is formalized as a Petri Net. The authors review the Graphplan notions of mutex relation and maintenance actions based on the Petri Net flow. They also classify pairs of conflicting actions in terms of four different control structures, which are used to build the Plan Net. In addition the authors present the order relation of propositions, i.e., pairs of conflicting propositions that allow inclusion of more information in the Planning Net. The planning problem represented on Planning Net is translated into a SAT instance and solved by a modern SAT solver. The authors show the advantages provided by the inclusion of the propositions ordering relation and compare their experimental results with Satplan.
{"title":"The Planning Net: A Structure to Improve Planning Solvers with Petri Nets","authors":"Marcos A. Schreiner, M. Castilho, Fabiano Silva, Luis Allan Künzle, R. Montaño","doi":"10.4018/ijncr.2015040102","DOIUrl":"https://doi.org/10.4018/ijncr.2015040102","url":null,"abstract":"In this paper the classical planning problem is formalized as a Petri Net. The authors review the Graphplan notions of mutex relation and maintenance actions based on the Petri Net flow. They also classify pairs of conflicting actions in terms of four different control structures, which are used to build the Plan Net. In addition the authors present the order relation of propositions, i.e., pairs of conflicting propositions that allow inclusion of more information in the Planning Net. The planning problem represented on Planning Net is translated into a SAT instance and solved by a modern SAT solver. The authors show the advantages provided by the inclusion of the propositions ordering relation and compare their experimental results with Satplan.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"46 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599154","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 : 2015-04-01DOI: 10.4018/ijncr.2015040104
Mateus Giesbrecht, C. Bottura
In this paper a recursive immuno inspired algorithm is proposed to identify time variant discrete multivariable dynamic systems. The main contribution of this paper has as starting point the idea that a multivariable dynamic system state space model can be seen as a point in a space defined by all possible matrices quadruples that define a state space model. With this in mind, the time variant discrete multivariable dynamic system modeling is transformed in an optimization problem and this problem is solved with an immuno inspired algorithm. To do that the inputs given to the system and the resulting outputs are divided in small sets containing data from small time intervals. These sets are defined as time windows, and for each window an immuno inspired optimization algorithm is applied to find the state space model that better represents the system at that time interval. The initial candidate solutions of each time interval are the ones of the last interval. The immuno inspired algorithm proposed in this paper has some modifications to the original Opt-AINet algorithm to deal with the constraints that are natural from the system identification problem and these modifications are also contributions of this paper. The method proposed in this paper was applied to identify a time variant benchmark system, resulting in a time variant model. The outputs estimated with this model are closer to the benchmark system outputs than the outputs estimated with models obtained by other known identification methods. The Markov parameters of the variant benchmark system are also reproduced by the time variant model found with the new method.
{"title":"Recursive Immuno-Inspired Algorithm for Time Variant Discrete Multivariable Dynamic System State Space Identification","authors":"Mateus Giesbrecht, C. Bottura","doi":"10.4018/ijncr.2015040104","DOIUrl":"https://doi.org/10.4018/ijncr.2015040104","url":null,"abstract":"In this paper a recursive immuno inspired algorithm is proposed to identify time variant discrete multivariable dynamic systems. The main contribution of this paper has as starting point the idea that a multivariable dynamic system state space model can be seen as a point in a space defined by all possible matrices quadruples that define a state space model. With this in mind, the time variant discrete multivariable dynamic system modeling is transformed in an optimization problem and this problem is solved with an immuno inspired algorithm. To do that the inputs given to the system and the resulting outputs are divided in small sets containing data from small time intervals. These sets are defined as time windows, and for each window an immuno inspired optimization algorithm is applied to find the state space model that better represents the system at that time interval. The initial candidate solutions of each time interval are the ones of the last interval. The immuno inspired algorithm proposed in this paper has some modifications to the original Opt-AINet algorithm to deal with the constraints that are natural from the system identification problem and these modifications are also contributions of this paper. The method proposed in this paper was applied to identify a time variant benchmark system, resulting in a time variant model. The outputs estimated with this model are closer to the benchmark system outputs than the outputs estimated with models obtained by other known identification methods. The Markov parameters of the variant benchmark system are also reproduced by the time variant model found with the new method.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131644946","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 : 2015-04-01DOI: 10.4018/ijncr.2015040103
G. F. Miranda, G. Giraldi, C. Thomaz, Daniel Millán
The Local Riemannian Manifold Learning (LRML) recovers the manifold topology and geometry behind database samples through normal coordinate neighborhoods computed by the exponential map. Besides, LRML uses barycentric coordinates to go from the parameter space to the Riemannian manifold in order to perform the manifold synthesis. Despite of the advantages of LRML, the obtained parameterization cannot be used as a representational space without ambiguities. Besides, the synthesis process needs a simplicial decomposition of the lower dimensional domain to be efficiently performed, which is not considered in the LRML proposal. In this paper, the authors address these drawbacks of LRML by using a composition procedure to combine the normal coordinate neighborhoods for building a suitable representational space. Moreover, they incorporate a polyhedral geometry framework to the LRML method to give an efficient background for the synthesis process and data analysis. In the computational experiments, the authors verify the efficiency of the LRML combined with the composition and discrete geometry frameworks for dimensionality reduction, synthesis and data exploration.
{"title":"Composition of Local Normal Coordinates and Polyhedral Geometry in Riemannian Manifold Learning","authors":"G. F. Miranda, G. Giraldi, C. Thomaz, Daniel Millán","doi":"10.4018/ijncr.2015040103","DOIUrl":"https://doi.org/10.4018/ijncr.2015040103","url":null,"abstract":"The Local Riemannian Manifold Learning (LRML) recovers the manifold topology and geometry behind database samples through normal coordinate neighborhoods computed by the exponential map. Besides, LRML uses barycentric coordinates to go from the parameter space to the Riemannian manifold in order to perform the manifold synthesis. Despite of the advantages of LRML, the obtained parameterization cannot be used as a representational space without ambiguities. Besides, the synthesis process needs a simplicial decomposition of the lower dimensional domain to be efficiently performed, which is not considered in the LRML proposal. In this paper, the authors address these drawbacks of LRML by using a composition procedure to combine the normal coordinate neighborhoods for building a suitable representational space. Moreover, they incorporate a polyhedral geometry framework to the LRML method to give an efficient background for the synthesis process and data analysis. In the computational experiments, the authors verify the efficiency of the LRML combined with the composition and discrete geometry frameworks for dimensionality reduction, synthesis and data exploration.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116688125","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 : 2014-10-01DOI: 10.4018/ijncr.2014100101
V. G. Silva, M. Tavakoli, Lino Marques
This paper demonstrates dexterity optimization of a three degrees of freedom (3 DOF) Delta manipulator. The parallel manipulator consists of three identical chains and is able to move on all three translational axes. In order to optimize the manipulator in term of dexterity, a floating point Genetic Algorithm (GA) global search method was applied. This algorithm intends to maximize the Global Condition Index (GCI) of the manipulator over its workspace and to propose the best design parameters such as the length of the links which result in a higher GCI and thus a better dexterity.
{"title":"Optimization of a Three Degrees of Freedom DELTA Manipulator for Well-Conditioned Workspace with a Floating Point Genetic Algorithm","authors":"V. G. Silva, M. Tavakoli, Lino Marques","doi":"10.4018/ijncr.2014100101","DOIUrl":"https://doi.org/10.4018/ijncr.2014100101","url":null,"abstract":"This paper demonstrates dexterity optimization of a three degrees of freedom (3 DOF) Delta manipulator. The parallel manipulator consists of three identical chains and is able to move on all three translational axes. In order to optimize the manipulator in term of dexterity, a floating point Genetic Algorithm (GA) global search method was applied. This algorithm intends to maximize the Global Condition Index (GCI) of the manipulator over its workspace and to propose the best design parameters such as the length of the links which result in a higher GCI and thus a better dexterity.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123337498","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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