Pub Date : 2018-07-01DOI: 10.1109/CEC.2018.8477827
J. Prieto, Elizabeth León Guzman, M. Garzon
DNA has emerged as a new computational resource for data encoding and processing. The fundamental problem of DNA Codeword Design (CWD) calls for finding effective ways to encode and process data in DNA. The problem has shown to be of interest in other areas as well, including computational memories, self-assembly and phylogenetic analysis, among others. In prior work, a framework to analyze this problem has been developed and simple versions of CWD have been shown to be NP-complete using any single reasonable metric that approximates the Gibbs energy, thus practically making it very difficult to find a general procedure for finding optimal efficient encodings. We present a Self-adaptive Evolutionary Algorithm for CWD (SaEA-CWD) as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA). SaEA-CWD is a parameter adaptation technique that automatically adapts the rates of its genetic operator applications to exploit structural properties of the search space to improve the speed and quality of the solutions. An implementation and preliminary results are evaluated in spaces where searches are already prohibitive to ordinary methods (such as 8- and 10-mers) due to the combinatorial explosion of the solution DNA space. Applications to other problems are suggested, such as a general technique for dimensionality reduction based on SaEA-CWD.
{"title":"Self-adaptive Evolutionary Algorithm for DNA Codeword Design","authors":"J. Prieto, Elizabeth León Guzman, M. Garzon","doi":"10.1109/CEC.2018.8477827","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477827","url":null,"abstract":"DNA has emerged as a new computational resource for data encoding and processing. The fundamental problem of DNA Codeword Design (CWD) calls for finding effective ways to encode and process data in DNA. The problem has shown to be of interest in other areas as well, including computational memories, self-assembly and phylogenetic analysis, among others. In prior work, a framework to analyze this problem has been developed and simple versions of CWD have been shown to be NP-complete using any single reasonable metric that approximates the Gibbs energy, thus practically making it very difficult to find a general procedure for finding optimal efficient encodings. We present a Self-adaptive Evolutionary Algorithm for CWD (SaEA-CWD) as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA). SaEA-CWD is a parameter adaptation technique that automatically adapts the rates of its genetic operator applications to exploit structural properties of the search space to improve the speed and quality of the solutions. An implementation and preliminary results are evaluated in spaces where searches are already prohibitive to ordinary methods (such as 8- and 10-mers) due to the combinatorial explosion of the solution DNA space. Applications to other problems are suggested, such as a general technique for dimensionality reduction based on SaEA-CWD.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116496576","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477886
Shijia Li, Sibo Feng, Ping Guo, Qian Yin
Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.
{"title":"A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection","authors":"Shijia Li, Sibo Feng, Ping Guo, Qian Yin","doi":"10.1109/CEC.2018.8477886","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477886","url":null,"abstract":"Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127602952","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 : 2018-07-01DOI: 10.1109/cec.2018.8477695
{"title":"Monday, July 9","authors":"","doi":"10.1109/cec.2018.8477695","DOIUrl":"https://doi.org/10.1109/cec.2018.8477695","url":null,"abstract":"","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127997433","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477979
N. Shvai, Antoine Meicler, A. Hasnat, Edouard Machover, P. Maarek, Stephane Loquet, A. Nakib
In this work, a challenging vehicle type classification problem for automatic toll collection task is considered, which is currently accomplished with an Optical Sensors (OS) and corrected manually. Indeed, the human operators are engaged to manually correct the OS misclassified vehicles by observing the images obtained from the camera. In this paper, we propose a novel vehicle classification algorithm, which first uses the camera images to obtain the vehicle class probabilities using several Convolutional Neural Networks (CNNs) models and then uses the Gradient Boosting based classifier to fuse the continuous class probabilities with the discrete class labels obtained from two optical sensors. We train and evaluate our method using a challenging dataset collected from the cameras of the toll collection points. Results show that our method performs significantly (98.22% compared to 75.11%) better than the existing automatic toll collection system and, hence will vastly reduce the workload of the human operators. Moreover, we provide an in-depth analysis w.r.t. the learning strategies:e.g., choice of the optimization algorithm of the CNN model. Our results and analysis highlights interesting perspectives and challenges for the future work.
{"title":"Optimal Ensemble Classifiers Based Classification for Automatic Vehicle Type Recognition","authors":"N. Shvai, Antoine Meicler, A. Hasnat, Edouard Machover, P. Maarek, Stephane Loquet, A. Nakib","doi":"10.1109/CEC.2018.8477979","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477979","url":null,"abstract":"In this work, a challenging vehicle type classification problem for automatic toll collection task is considered, which is currently accomplished with an Optical Sensors (OS) and corrected manually. Indeed, the human operators are engaged to manually correct the OS misclassified vehicles by observing the images obtained from the camera. In this paper, we propose a novel vehicle classification algorithm, which first uses the camera images to obtain the vehicle class probabilities using several Convolutional Neural Networks (CNNs) models and then uses the Gradient Boosting based classifier to fuse the continuous class probabilities with the discrete class labels obtained from two optical sensors. We train and evaluate our method using a challenging dataset collected from the cameras of the toll collection points. Results show that our method performs significantly (98.22% compared to 75.11%) better than the existing automatic toll collection system and, hence will vastly reduce the workload of the human operators. Moreover, we provide an in-depth analysis w.r.t. the learning strategies:e.g., choice of the optimization algorithm of the CNN model. Our results and analysis highlights interesting perspectives and challenges for the future work.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132534003","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477671
A. Pawlovsky
We introduce an immune algorithm (IA) that for the generation of a cell for a new (immune candidate) cell group uses an evolutionary scheme that makes the cell inherit receptors from more than two other cells. This IA is used to find combinations of features (components) that could improve the accuracy of a kNN (k Nearest Neighbor) when it is used for diagnosis or prognosis. We evaluated our approach with five medical data sets and found that it effectively helps to improve the accuracy of kNN in more than 2%.
{"title":"An Immune Algorithm with an Evolutionary Scheme for Component Selection for the kNN Method","authors":"A. Pawlovsky","doi":"10.1109/CEC.2018.8477671","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477671","url":null,"abstract":"We introduce an immune algorithm (IA) that for the generation of a cell for a new (immune candidate) cell group uses an evolutionary scheme that makes the cell inherit receptors from more than two other cells. This IA is used to find combinations of features (components) that could improve the accuracy of a kNN (k Nearest Neighbor) when it is used for diagnosis or prognosis. We evaluated our approach with five medical data sets and found that it effectively helps to improve the accuracy of kNN in more than 2%.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132657686","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477738
Nicolas D. Griffiths Sanchez, P. A. Vargas, M. Couceiro
This work focuses on the development of an autonomous multi-robot strategy to explore unknown underwater environments by collecting data about water properties and the existence of obstacles. Unknown underwater spaces are hostile environments whose exploration is often a complex, high-risk undertaking. The use of human divers or manned vehicles for these scenarios involves significant risk and enormous overheads. The systems currently employed for such tasks usually rely on remotely operated vehicles (ROVs), which are controlled by a human operator. The problems associated with this approach include the considerable costs of hiring a highly trained operator, the required presence of a manned vehicle in close proximity to the ROV, and the lag in communication often experienced between the operator and the ROV. This work proposes the use of autonomous robots, as opposed to human divers, which would enable costs to be substantially reduced. Likewise, a distributed swarm approach would allow the environment to be explored more rapidly and more efficiently than when using a single robot. The swarm strategy described in this work is based on Robotic Darwinian Particle Swarm Optimization (RDPSO), which was initially designed for planar robotic ground applications. This is the first study to generalize the RPSO algorithm for 3D applications, focusing on underwater robotics with the aim of providing a higher exploration speed and improved robustness to individual failures when compared to traditional single ROV approaches.
{"title":"A Darwinian Swarm Robotics Strategy Applied to Underwater Exploration","authors":"Nicolas D. Griffiths Sanchez, P. A. Vargas, M. Couceiro","doi":"10.1109/CEC.2018.8477738","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477738","url":null,"abstract":"This work focuses on the development of an autonomous multi-robot strategy to explore unknown underwater environments by collecting data about water properties and the existence of obstacles. Unknown underwater spaces are hostile environments whose exploration is often a complex, high-risk undertaking. The use of human divers or manned vehicles for these scenarios involves significant risk and enormous overheads. The systems currently employed for such tasks usually rely on remotely operated vehicles (ROVs), which are controlled by a human operator. The problems associated with this approach include the considerable costs of hiring a highly trained operator, the required presence of a manned vehicle in close proximity to the ROV, and the lag in communication often experienced between the operator and the ROV. This work proposes the use of autonomous robots, as opposed to human divers, which would enable costs to be substantially reduced. Likewise, a distributed swarm approach would allow the environment to be explored more rapidly and more efficiently than when using a single robot. The swarm strategy described in this work is based on Robotic Darwinian Particle Swarm Optimization (RDPSO), which was initially designed for planar robotic ground applications. This is the first study to generalize the RPSO algorithm for 3D applications, focusing on underwater robotics with the aim of providing a higher exploration speed and improved robustness to individual failures when compared to traditional single ROV approaches.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922422","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477678
Bingyam Mao, Zhijiang Xie, Yongbo Wang, Huapeng Wu, H. Handroos
The artificial bee colony (ABC) algorithm is a heuristic optimization algorithm based on the behavior of honeybee swarms. Inspired by particle swarm optimization (PSO) and differential evolution (DE) algorithms, we propose an improved ABC algorithm, named SAG-ABC, which incorporates a self-adaptive employed bees and guard stage to construct a more efficient algorithm. This algorithm combines the advantages of ABC algorithm, which has good exploration capability and global search ability and ease of implementation with fewer control parameters, DE and PSO algorithm, which exchange information with several individuals and utilize the history best information. The searching strategies in these different swarm intelligent algorithms are presented. The information is exchanged among individuals or elements. For the new SAG-ABC algorithm, the self-adaptive employed bees are guided by the global history best bee to enable search in a wider area. Then the search results are adapted to a smaller area. The guard stage is applied to improve the search performance of the employed bees phase by controlling the frequency with which the employed bees abandon the food source. Comparisons between the PSO algorithm, DE algorithm and ABC algorithm are made based on 16 benchmark functions. The results demonstrate the good performance and searching ability of the proposed algorithm.
{"title":"A Self-adaptive Artificial Bee Colony Algorithm with Guard Stage for Global Optimization","authors":"Bingyam Mao, Zhijiang Xie, Yongbo Wang, Huapeng Wu, H. Handroos","doi":"10.1109/CEC.2018.8477678","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477678","url":null,"abstract":"The artificial bee colony (ABC) algorithm is a heuristic optimization algorithm based on the behavior of honeybee swarms. Inspired by particle swarm optimization (PSO) and differential evolution (DE) algorithms, we propose an improved ABC algorithm, named SAG-ABC, which incorporates a self-adaptive employed bees and guard stage to construct a more efficient algorithm. This algorithm combines the advantages of ABC algorithm, which has good exploration capability and global search ability and ease of implementation with fewer control parameters, DE and PSO algorithm, which exchange information with several individuals and utilize the history best information. The searching strategies in these different swarm intelligent algorithms are presented. The information is exchanged among individuals or elements. For the new SAG-ABC algorithm, the self-adaptive employed bees are guided by the global history best bee to enable search in a wider area. Then the search results are adapted to a smaller area. The guard stage is applied to improve the search performance of the employed bees phase by controlling the frequency with which the employed bees abandon the food source. Comparisons between the PSO algorithm, DE algorithm and ABC algorithm are made based on 16 benchmark functions. The results demonstrate the good performance and searching ability of the proposed algorithm.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151066","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477969
Xianqi Chen, Wen Yao, Yong Zhao, Xiaoqian Chen, Jun Zhang, Yazhong Luo
The satellite layout optimization design (SLOD) problem is a kind of three-dimensional layout problems with complex performance constraints and known as a NP-hard problem. To solve SLOD problems efficiently and effectively, two types of hybrid optimization algorithm based on differential evolution (DE) are proposed in this paper. Concerning the design requirements of satellite attitude control subsystem, the SLOD problem is formulated, aiming to improve the overall mass characteristics of satellite. To explore the layout design space globally, the DE algorithm is utilized as the main framework of the proposed hybrid algorithm. Then in order to improve the local exploitation capability and algorithm robustness, sequential quadratic programming (SQP), as a gradient-based method, is combined with DE in two unique ways, comprising two types of hybrid algorithm. In the first type of hybrid algorithm (denoted by DESQP), SQP is performed when iteration process of DE has finished and only the final solution of DE is used as the initial point of SQP, the purpose of which is to locate the most promising area of optimum with DE first and then make a rapid exploitation around the quasi-optimum. In the second type of hybrid algorithm (denoted by DESQPDE), SQP is performed in the specific iteration of DE and all the current-generation population individuals are used as the initial points, the purpose of which is to accelerate the evolution process while holding the diversity of the population and to enhance the robustness. Finally, the efficacy and robustness of the proposed hybrid algorithms are compared with classical DE and also validated by two three-dimensional satellite layout cases with 14 and 40 components, respectively.
{"title":"The Hybrid Algorithms Based on Differential Evolution for Satellite Layout Optimization Design","authors":"Xianqi Chen, Wen Yao, Yong Zhao, Xiaoqian Chen, Jun Zhang, Yazhong Luo","doi":"10.1109/CEC.2018.8477969","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477969","url":null,"abstract":"The satellite layout optimization design (SLOD) problem is a kind of three-dimensional layout problems with complex performance constraints and known as a NP-hard problem. To solve SLOD problems efficiently and effectively, two types of hybrid optimization algorithm based on differential evolution (DE) are proposed in this paper. Concerning the design requirements of satellite attitude control subsystem, the SLOD problem is formulated, aiming to improve the overall mass characteristics of satellite. To explore the layout design space globally, the DE algorithm is utilized as the main framework of the proposed hybrid algorithm. Then in order to improve the local exploitation capability and algorithm robustness, sequential quadratic programming (SQP), as a gradient-based method, is combined with DE in two unique ways, comprising two types of hybrid algorithm. In the first type of hybrid algorithm (denoted by DESQP), SQP is performed when iteration process of DE has finished and only the final solution of DE is used as the initial point of SQP, the purpose of which is to locate the most promising area of optimum with DE first and then make a rapid exploitation around the quasi-optimum. In the second type of hybrid algorithm (denoted by DESQPDE), SQP is performed in the specific iteration of DE and all the current-generation population individuals are used as the initial points, the purpose of which is to accelerate the evolution process while holding the diversity of the population and to enhance the robustness. Finally, the efficacy and robustness of the proposed hybrid algorithms are compared with classical DE and also validated by two three-dimensional satellite layout cases with 14 and 40 components, respectively.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540547","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477799
H. Shishido, J. C. Estrella, C. Toledo
Cloud computing provides infrastructure for executing workflows that require high processing and storage capacity. Although there are several algorithms for scheduling workflows, few consider security criterion. Algorithms that cover security usually optimize either cost or makespan. However, there are cases where the user would like to choose or evaluate among different solutions that present a trade-off between monetary cost and execution time (makespan) of the workflow. The selection of the tasks, which involve confidential/sensitive data, has to prioritize the safe execution of the workflow. In this paper, we propose a multi-objective optimization for scheduling of workflow tasks in cloud environments by considering cost and makespan under different task selection policies. Extensive experiments in real-world workflows with different policies show that our approach returns several solutions in the Pareto frontier for both cost and makespan. The results revealed a reasonable ability to find Pareto frontiers during the optimization process.
{"title":"Multi-Objective Optimization for Workflow Scheduling Under Task Selection Policies in Clouds","authors":"H. Shishido, J. C. Estrella, C. Toledo","doi":"10.1109/CEC.2018.8477799","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477799","url":null,"abstract":"Cloud computing provides infrastructure for executing workflows that require high processing and storage capacity. Although there are several algorithms for scheduling workflows, few consider security criterion. Algorithms that cover security usually optimize either cost or makespan. However, there are cases where the user would like to choose or evaluate among different solutions that present a trade-off between monetary cost and execution time (makespan) of the workflow. The selection of the tasks, which involve confidential/sensitive data, has to prioritize the safe execution of the workflow. In this paper, we propose a multi-objective optimization for scheduling of workflow tasks in cloud environments by considering cost and makespan under different task selection policies. Extensive experiments in real-world workflows with different policies show that our approach returns several solutions in the Pareto frontier for both cost and makespan. The results revealed a reasonable ability to find Pareto frontiers during the optimization process.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114783298","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 : 2018-07-01DOI: 10.1109/CEC.2018.8477819
Victor Cunha, Luciana S. Pessoa, M. Vellasco, R. Tanscheit, M. Pacheco
The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.
{"title":"A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem","authors":"Victor Cunha, Luciana S. Pessoa, M. Vellasco, R. Tanscheit, M. Pacheco","doi":"10.1109/CEC.2018.8477819","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477819","url":null,"abstract":"The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114987743","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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