Pub Date : 2014-10-01DOI: 10.4018/IJNCR.2014100103
António Leitão, Adriano Vinhas, P. Machado, Francisco C. Pereira
Inverse Combinatorial Optimization has become a relevant research subject over the past decades. In graph theory, the Inverse Shortest Path Length problem becomes relevant when people don't have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.
{"title":"A Genetic Algorithms Approach for Inverse Shortest Path Length Problems","authors":"António Leitão, Adriano Vinhas, P. Machado, Francisco C. Pereira","doi":"10.4018/IJNCR.2014100103","DOIUrl":"https://doi.org/10.4018/IJNCR.2014100103","url":null,"abstract":"Inverse Combinatorial Optimization has become a relevant research subject over the past decades. In graph theory, the Inverse Shortest Path Length problem becomes relevant when people don't have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"19 2 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":"125540257","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.2014100104
Rui L. Lopes, E. Costa
Evolutionary Algorithms (EA) approach differently from nature the genotype-phenotype relationship, a view that is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, the authors revisit the Regulatory Network Computational Device (ReNCoDe), now empowered with feedback connections, providing a formal demonstration of the typical solutions evolved with this representation. The authors also present some preliminary results of using a variant of the model to deal with problems with multiple outputs.
{"title":"Developments on the Regulatory Network Computational Device","authors":"Rui L. Lopes, E. Costa","doi":"10.4018/ijncr.2014100104","DOIUrl":"https://doi.org/10.4018/ijncr.2014100104","url":null,"abstract":"Evolutionary Algorithms (EA) approach differently from nature the genotype-phenotype relationship, a view that is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, the authors revisit the Regulatory Network Computational Device (ReNCoDe), now empowered with feedback connections, providing a formal demonstration of the typical solutions evolved with this representation. The authors also present some preliminary results of using a variant of the model to deal with problems with multiple outputs.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"95 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":"125583365","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.2014100102
A. Nogueira, J. Ribeiro, F. F. Vega, M. Z. Rela
In Object-Oriented Evolutionary Testing, metaheuristics are employed to select or generate Test Data for Object-Oriented software. The application of search-based strategies to the Software Testing of Object-Oriented Software is fairly recent and is yet to be investigated comprehensively; this article aims to explore, review and contextualize relevant literature and research in this area, while pinpointing open problems and setting grounds for future work.
{"title":"Object-Oriented Evolutionary Testing: A Review of Evolutionary Approaches to the Generation of Test Data for Object-Oriented Software","authors":"A. Nogueira, J. Ribeiro, F. F. Vega, M. Z. Rela","doi":"10.4018/ijncr.2014100102","DOIUrl":"https://doi.org/10.4018/ijncr.2014100102","url":null,"abstract":"In Object-Oriented Evolutionary Testing, metaheuristics are employed to select or generate Test Data for Object-Oriented software. The application of search-based strategies to the Software Testing of Object-Oriented Software is fairly recent and is yet to be investigated comprehensively; this article aims to explore, review and contextualize relevant literature and research in this area, while pinpointing open problems and setting grounds for future work.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"104 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":"133907736","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 : 2014-07-01DOI: 10.4018/ijncr.2014070101
A. Fuente
Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be 'condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.
{"title":"Condensing Biochemistry into Gene Regulatory Networks","authors":"A. Fuente","doi":"10.4018/ijncr.2014070101","DOIUrl":"https://doi.org/10.4018/ijncr.2014070101","url":null,"abstract":"Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be 'condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131334148","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 : 2014-07-01DOI: 10.4018/ijncr.2014070102
G. Dounias, V. Vassiliadis
The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.
{"title":"Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems: A Survey","authors":"G. Dounias, V. Vassiliadis","doi":"10.4018/ijncr.2014070102","DOIUrl":"https://doi.org/10.4018/ijncr.2014070102","url":null,"abstract":"The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115534443","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 : 2014-07-01DOI: 10.4018/ijncr.2014070105
Roman V Yampolskiy, Nawaf Ali, Darryl D'Souza, Abdallah A. Mohamed
This work categorizes and reviews behavioral biometrics with the inclusion of future-oriented techniques. A general introduction to this field is given alongside the benefits of this non-intrusive approach. It presents the examination and analysis of the current research in the field and the different types of behavior-centric features. Accuracy rates for verifying users with different behavioral biometric approaches are compared. Privacy issues that will or may arise in the future with behavioral biometrics are also addressed. Finally, the general properties of behavior, the influence of environmental factors on observed behavior and the potential directions for future research in the field of behavioral biometrics are discussed.
{"title":"Behavioral Biometrics: Categorization and Review","authors":"Roman V Yampolskiy, Nawaf Ali, Darryl D'Souza, Abdallah A. Mohamed","doi":"10.4018/ijncr.2014070105","DOIUrl":"https://doi.org/10.4018/ijncr.2014070105","url":null,"abstract":"This work categorizes and reviews behavioral biometrics with the inclusion of future-oriented techniques. A general introduction to this field is given alongside the benefits of this non-intrusive approach. It presents the examination and analysis of the current research in the field and the different types of behavior-centric features. Accuracy rates for verifying users with different behavioral biometric approaches are compared. Privacy issues that will or may arise in the future with behavioral biometrics are also addressed. Finally, the general properties of behavior, the influence of environmental factors on observed behavior and the potential directions for future research in the field of behavioral biometrics are discussed.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126051354","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 : 2014-07-01DOI: 10.4018/ijncr.2014070103
E. Kldiashvili
The field of healthcare informatics is rapidly evolving. The new models and protocols of medical information system (MIS) are developed. Despite obvious advantages and benefits, practical application of MIS in everyday practice is slow. Cloud computing have emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale sharing, innovative applications, and, in some cases, high-performance orientation. "Cloud computing" the authors are going to define as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Cloud computing is a new way of delivering computing resources and services. It is plausible, that this technology has more potential and can improve health care services, benefit health care research, and change the face of health information technology. This can be solution for widespread and effective implementation of the medical information system. The present article will discuss the application of cloud computing for the medical information system practical usage. The ideal of healthcare in the information age must be to create knowledge from medical information and less time managing medical information and data. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for medical informatics applications. The usage of MIS holds the potential to improve, develop and realize medical service in the effective and comprehensive mode.
{"title":"Application of the Cloud Computing for the Effective Implementation of the Medical Information System","authors":"E. Kldiashvili","doi":"10.4018/ijncr.2014070103","DOIUrl":"https://doi.org/10.4018/ijncr.2014070103","url":null,"abstract":"The field of healthcare informatics is rapidly evolving. The new models and protocols of medical information system (MIS) are developed. Despite obvious advantages and benefits, practical application of MIS in everyday practice is slow. Cloud computing have emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale sharing, innovative applications, and, in some cases, high-performance orientation. \"Cloud computing\" the authors are going to define as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Cloud computing is a new way of delivering computing resources and services. It is plausible, that this technology has more potential and can improve health care services, benefit health care research, and change the face of health information technology. This can be solution for widespread and effective implementation of the medical information system. The present article will discuss the application of cloud computing for the medical information system practical usage. The ideal of healthcare in the information age must be to create knowledge from medical information and less time managing medical information and data. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for medical informatics applications. The usage of MIS holds the potential to improve, develop and realize medical service in the effective and comprehensive mode.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128422206","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 : 2014-07-01DOI: 10.4018/ijncr.2014070104
Tony Tung, T. Matsuyama
Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.
{"title":"Visual Tracking Using Multimodal Particle Filter","authors":"Tony Tung, T. Matsuyama","doi":"10.4018/ijncr.2014070104","DOIUrl":"https://doi.org/10.4018/ijncr.2014070104","url":null,"abstract":"Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130854171","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 : 2014-04-01DOI: 10.4018/ijncr.2014040101
Jorge C. Gomes, P. Urbano, A. Christensen
Novelty search is an evolutionary approach in which the population is driven towards behavioural innovation instead of towards a fixed objective. The use of behavioural novelty to score candidate solutions precludes convergence to local optima. However, in novelty search, significant effort may be spent on exploration of novel, but unfit behaviours. We propose progressive minimal criteria novelty search (PMCNS) to overcome this issue. In PMCNS, novelty search can freely explore the behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach by evolving neurocontrollers for swarms of robots in two distinct tasks. Our results show that PMCNS outperforms fitness-based evolution and pure novelty search, and that PMCNS is superior to linear scalarisation of novelty and fitness scores. An analysis of behaviour space exploration shows that the benefits of novelty search are conserved in PMCNS despite the evolutionary pressure towards progressively fitter behaviours.
{"title":"PMCNS: Using a Progressively Stricter Fitness Criterion to Guide Novelty Search","authors":"Jorge C. Gomes, P. Urbano, A. Christensen","doi":"10.4018/ijncr.2014040101","DOIUrl":"https://doi.org/10.4018/ijncr.2014040101","url":null,"abstract":"Novelty search is an evolutionary approach in which the population is driven towards behavioural innovation instead of towards a fixed objective. The use of behavioural novelty to score candidate solutions precludes convergence to local optima. However, in novelty search, significant effort may be spent on exploration of novel, but unfit behaviours. We propose progressive minimal criteria novelty search (PMCNS) to overcome this issue. In PMCNS, novelty search can freely explore the behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach by evolving neurocontrollers for swarms of robots in two distinct tasks. Our results show that PMCNS outperforms fitness-based evolution and pure novelty search, and that PMCNS is superior to linear scalarisation of novelty and fitness scores. An analysis of behaviour space exploration shows that the benefits of novelty search are conserved in PMCNS despite the evolutionary pressure towards progressively fitter behaviours.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121359270","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 : 2014-04-01DOI: 10.4018/ijncr.2014040103
Jesús-Antonio Hernández-Riveros, J. Urrea-Quintero
The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.
{"title":"SOSPD Controllers Tuning by Means of an Evolutionary Algorithm","authors":"Jesús-Antonio Hernández-Riveros, J. Urrea-Quintero","doi":"10.4018/ijncr.2014040103","DOIUrl":"https://doi.org/10.4018/ijncr.2014040103","url":null,"abstract":"The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127730967","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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