Pub Date : 2018-07-01DOI: 10.1109/CEC.2018.8477858
T. Covões, Eduardo R. Hruschka
Constrained clustering has been an active research topic in the last decade. Among the different kinds of constraints, must-link and cannot-link are the most adopted ones. However, most algorithms assume that the number of clusters are known a priori. Besides this usually unrealistic assumption, one often ignores the fact that must-link constraints may correspond to objects in different density regions of the input space, thereby requiring a more complex structure to represent the underlying concept. Aimed at overcoming these limitations, we present the Feasible-Infeasible Evolutionary Create & Eliminate for Expectation Maximization (FIECE-EM), which identifies a Gaussian Mixture Model that is a good fit for the data, while meeting the constraints provided. We compare FIECE-EM with a state-of-the-art algorithm. Our results indicate that FIECE-EM obtains competitive results, without the need for fine-tuning a tradeoff parameter as in the state-of-the-art algorithm under comparison.
{"title":"Classification with Multi-Modal Classes Using Evolutionary Algorithms and Constrained Clustering","authors":"T. Covões, Eduardo R. Hruschka","doi":"10.1109/CEC.2018.8477858","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477858","url":null,"abstract":"Constrained clustering has been an active research topic in the last decade. Among the different kinds of constraints, must-link and cannot-link are the most adopted ones. However, most algorithms assume that the number of clusters are known a priori. Besides this usually unrealistic assumption, one often ignores the fact that must-link constraints may correspond to objects in different density regions of the input space, thereby requiring a more complex structure to represent the underlying concept. Aimed at overcoming these limitations, we present the Feasible-Infeasible Evolutionary Create & Eliminate for Expectation Maximization (FIECE-EM), which identifies a Gaussian Mixture Model that is a good fit for the data, while meeting the constraints provided. We compare FIECE-EM with a state-of-the-art algorithm. Our results indicate that FIECE-EM obtains competitive results, without the need for fine-tuning a tradeoff parameter as in the state-of-the-art algorithm under comparison.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"64 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":"126408706","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.2018.8477913
Grant Daly, Ryan G. Benton, T. Johnsten
Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as “age”, may not be changed, whereas flexible attributes, such as “interest rate”, may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated; this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called Multi-Objective Evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as “rare”, while achieving good computational performance.
{"title":"A Multi-Objective Evolutionary Action Rule Mining Method","authors":"Grant Daly, Ryan G. Benton, T. Johnsten","doi":"10.1109/CEC.2018.8477913","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477913","url":null,"abstract":"Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as “age”, may not be changed, whereas flexible attributes, such as “interest rate”, may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated; this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called Multi-Objective Evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as “rare”, while achieving good computational performance.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"43 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":"114192003","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.2018.8477862
A. Tahmassebi, A. Gandomi, A. Meyer-Bäse
Massive Online Open Course (MOOC) is a scalable, free or affordable online course which emerged as one of the fastest growing distance education platforms in the past decade. One of the biggest challenges that threatens distance education is abnormality in the overall level of consciousness of students while they are taking the course. In this paper, an evolutionary online framework was proposed to improve the performance of MOOCs via noninvasive electro-physiological monitoring methods such as electroencephalography (EEG). Based on the proposed platform, EEG signals can be recorded from users while they are wearing any EEG headsets. EEG measures a brain's spontaneous voltage fluctuations resulting from ionic current within the neurons of the brain via multiple electrodes placed on the scalp. A total of eleven extracted features from EEG signals were employed as the inputs of the evolutionary classification algorithm to predict two classes of confused and not-confused for each individual. An accuracy of 89 % was considered significant enough to suggest that there is difference in the EEG signals of individuals with confusion versus not-confused individuals.
{"title":"An Evolutionary Online Framework for MOOC Performance Using EEG Data","authors":"A. Tahmassebi, A. Gandomi, A. Meyer-Bäse","doi":"10.1109/CEC.2018.8477862","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477862","url":null,"abstract":"Massive Online Open Course (MOOC) is a scalable, free or affordable online course which emerged as one of the fastest growing distance education platforms in the past decade. One of the biggest challenges that threatens distance education is abnormality in the overall level of consciousness of students while they are taking the course. In this paper, an evolutionary online framework was proposed to improve the performance of MOOCs via noninvasive electro-physiological monitoring methods such as electroencephalography (EEG). Based on the proposed platform, EEG signals can be recorded from users while they are wearing any EEG headsets. EEG measures a brain's spontaneous voltage fluctuations resulting from ionic current within the neurons of the brain via multiple electrodes placed on the scalp. A total of eleven extracted features from EEG signals were employed as the inputs of the evolutionary classification algorithm to predict two classes of confused and not-confused for each individual. An accuracy of 89 % was considered significant enough to suggest that there is difference in the EEG signals of individuals with confusion versus not-confused individuals.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"79 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":"121093200","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.2018.8477773
Xiaohan Bai, Xiaoying Gao, Bing Xue
Text mining is an important and popular data mining topic, where a fundamental objective is to enable users to extract informative data from text-based assets and perform related operations on the text, like retrieval, classification, and summarization. For text classification, one of the most important steps is feature selection, because not all the features in the text dataset are useful for classification. Irrelevant and redundant features should be removed to increase the accuracy and decrease the complexity and running time, but it is often an expensive process, and most existing methods using a simple filter to remove features, which might potentially loose some useful ones because of feature interactions. Furthermore, there is little research using particle swarm optimization (PSO) algorithms to select informative features for text classification. This paper presents an approach using a novel two-stage method for text feature selection, where with the features selected by four different filter ranking methods at the first stage, more irrelevant features are removed by PSO to compose the final feature subset. The proposed algorithm is compared with four traditional feature selection methods on the commonly used Reuter-21578 dataset. The experimental results show that the proposed two-stage method can substantially reduce the dimensionality of the feature space and improve the classification accuracy.
{"title":"Particle Swarm Optimization Based Two-Stage Feature Selection in Text Mining","authors":"Xiaohan Bai, Xiaoying Gao, Bing Xue","doi":"10.1109/CEC.2018.8477773","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477773","url":null,"abstract":"Text mining is an important and popular data mining topic, where a fundamental objective is to enable users to extract informative data from text-based assets and perform related operations on the text, like retrieval, classification, and summarization. For text classification, one of the most important steps is feature selection, because not all the features in the text dataset are useful for classification. Irrelevant and redundant features should be removed to increase the accuracy and decrease the complexity and running time, but it is often an expensive process, and most existing methods using a simple filter to remove features, which might potentially loose some useful ones because of feature interactions. Furthermore, there is little research using particle swarm optimization (PSO) algorithms to select informative features for text classification. This paper presents an approach using a novel two-stage method for text feature selection, where with the features selected by four different filter ranking methods at the first stage, more irrelevant features are removed by PSO to compose the final feature subset. The proposed algorithm is compared with four traditional feature selection methods on the commonly used Reuter-21578 dataset. The experimental results show that the proposed two-stage method can substantially reduce the dimensionality of the feature space and improve the classification accuracy.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"23 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":"116147529","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.2018.8477680
Diego L. Cavalca, R. Fernandes
In the globalized world, with highly competitive markets, companies are looking for ways to reduce costs in a sustainable manner, optimizing their production lines to increase their economic advantages. Thus, several studies appeared with the objective of modeling the productive sectors, among which it is possible to highlight the Flexible Job-Shop. This model aims to efficiently organize the distribution of tasks to be processed in a set of available machines so that the complete execution of these tasks takes the shortest possible time considering several productive constraints. The resolution of this model involves complex combinatorial calculations, which allow the development of computational tools for this purpose, supporting the decision-making process. Therefore, this work presents a hybrid computational proposal based on Particle Swarm Optimization and Simulated Annealing algorithms to use the intrinsic advantages of these approaches to scheduling industrial productions. The results show that the proposed hybrid algorithm efficiently solves the production scheduling problem in a partially flexible scenario, overcoming the minimization of the production completeness time present in some benchmarks found in the literature for this class of problems.
{"title":"Hybrid Particle Swarm Algorithm Applied to Flexible Job-Shop Problem","authors":"Diego L. Cavalca, R. Fernandes","doi":"10.1109/CEC.2018.8477680","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477680","url":null,"abstract":"In the globalized world, with highly competitive markets, companies are looking for ways to reduce costs in a sustainable manner, optimizing their production lines to increase their economic advantages. Thus, several studies appeared with the objective of modeling the productive sectors, among which it is possible to highlight the Flexible Job-Shop. This model aims to efficiently organize the distribution of tasks to be processed in a set of available machines so that the complete execution of these tasks takes the shortest possible time considering several productive constraints. The resolution of this model involves complex combinatorial calculations, which allow the development of computational tools for this purpose, supporting the decision-making process. Therefore, this work presents a hybrid computational proposal based on Particle Swarm Optimization and Simulated Annealing algorithms to use the intrinsic advantages of these approaches to scheduling industrial productions. The results show that the proposed hybrid algorithm efficiently solves the production scheduling problem in a partially flexible scenario, overcoming the minimization of the production completeness time present in some benchmarks found in the literature for this class of problems.","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":"123806125","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.2018.8477867
M. Baioletti, A. Milani, V. Santucci
Crossover operators are very important tools in Evolutionary Computation. Here we are interested in crossovers for the permutation representation that find applications in combinatorial optimization problems such as the permutation flowshop scheduling and the traveling salesman problem. We introduce three families of permutation crossovers based on algebraic properties of the permutation space. In particular, we exploit the group and lattice structures of the space. A total of 14 new crossovers is provided. Algebraic and semantic properties of the operators are discussed, while their performances are investigated by experimentally comparing them with known permutation crossovers on standard benchmarks from four popular permutation problems. Three different experimental scenarios are considered and the results clearly validate our proposals.
{"title":"Algebraic Crossover Operators for Permutations","authors":"M. Baioletti, A. Milani, V. Santucci","doi":"10.1109/CEC.2018.8477867","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477867","url":null,"abstract":"Crossover operators are very important tools in Evolutionary Computation. Here we are interested in crossovers for the permutation representation that find applications in combinatorial optimization problems such as the permutation flowshop scheduling and the traveling salesman problem. We introduce three families of permutation crossovers based on algebraic properties of the permutation space. In particular, we exploit the group and lattice structures of the space. A total of 14 new crossovers is provided. Algebraic and semantic properties of the operators are discussed, while their performances are investigated by experimentally comparing them with known permutation crossovers on standard benchmarks from four popular permutation problems. Three different experimental scenarios are considered and the results clearly validate our proposals.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"50 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":"124955736","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.2018.8477698
J. M. Freitas, F. R. D. Souza, H. Bernardino
Video games mimic real-world situations and they can be used as a benchmark to evaluate computational methods in solving different types of problems. Also, machine learning methods are used nowadays to improve the quality of non-player characters in order (i) to create human like behaviors, and (ii) to increase the hardness of the games. Genetic Programming (GP) has presented good results when evolving programs in general. One of the main advantage of GP is the availability of the source-code of its solutions, helping researchers to understand the decision-making process. Also, a formal grammar can be used in order to facilitate the generation of programs in more complex languages (such as Java, C, and Python). Here, we propose the use of Grammar-based Genetic Programming (GGP) to evolve controllers for Mario AI, a popular platform to test video game controllers which simulates the Nintendo's Super Mario Bros. Also, as GP provides the source-code of the solutions, we present and analyze the best program obtained. Finally, GGP is compared to other techniques from the literature and the results show that GGP find good controllers, specially with respect to the scores obtained on higher difficulty levels.
{"title":"Evolving Controllers for Mario AI Using Grammar-based Genetic Programming","authors":"J. M. Freitas, F. R. D. Souza, H. Bernardino","doi":"10.1109/CEC.2018.8477698","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477698","url":null,"abstract":"Video games mimic real-world situations and they can be used as a benchmark to evaluate computational methods in solving different types of problems. Also, machine learning methods are used nowadays to improve the quality of non-player characters in order (i) to create human like behaviors, and (ii) to increase the hardness of the games. Genetic Programming (GP) has presented good results when evolving programs in general. One of the main advantage of GP is the availability of the source-code of its solutions, helping researchers to understand the decision-making process. Also, a formal grammar can be used in order to facilitate the generation of programs in more complex languages (such as Java, C, and Python). Here, we propose the use of Grammar-based Genetic Programming (GGP) to evolve controllers for Mario AI, a popular platform to test video game controllers which simulates the Nintendo's Super Mario Bros. Also, as GP provides the source-code of the solutions, we present and analyze the best program obtained. Finally, GGP is compared to other techniques from the literature and the results show that GGP find good controllers, specially with respect to the scores obtained on higher difficulty levels.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"15 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":"122488572","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.2018.8477954
Adnan Amin, B. Shah, A. Khattak, T. Baker, Hamood ur Rahman Durani, S. Anwar
Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.
{"title":"Just-in-time Customer Churn Prediction: With and Without Data Transformation","authors":"Adnan Amin, B. Shah, A. Khattak, T. Baker, Hamood ur Rahman Durani, S. Anwar","doi":"10.1109/CEC.2018.8477954","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477954","url":null,"abstract":"Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"23 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":"122819954","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.2018.8477775
Sajib K. Biswas, Rishi Jagdev, Pranab K. Muhuri
In this paper, we have proposed an archived simulated annealing based novel approach for solving multi-objective energy-efficient scheduling on heterogeneous DVS activated processors in high-performance real-time systems. Real-time task scheduling problem is a well-known NP-hard problem. In these systems, tasks are usually associated with deadlines and represented by directed acyclic graphs since they depend on each other. So, system designers face difficulty in finding suitable solutions that can satisfy all the objectives of task scheduling, as warranted for proficient operations of such systems. Hence, this paper introduces a novel algorithm, called archived multi-objective simulated annealing for energy-efficient real-time scheduling (AMOSA-E2RTS) that finds an optimal schedule satisfying the precedence and deadline constraints. In the proposed algorithm, a domination concept leads towards finding the optimal trade-off solutions and tasks are prioritized according to three different policies i.e., latest deadline first (LDF), execution ranking and energy ranking policy. A suitable numerical example is used to demonstrate the working of the proposed approach. Experimental findings suggest that the proposed algorithm is capable of producing energy efficient scheduling decisions which satisfy all related constraints. Statistical analysis of the results has been conducted.
{"title":"Energy Efficient Scheduling in Multiprocessor Systems Using Archived Multi-objective Simulated Annealing","authors":"Sajib K. Biswas, Rishi Jagdev, Pranab K. Muhuri","doi":"10.1109/CEC.2018.8477775","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477775","url":null,"abstract":"In this paper, we have proposed an archived simulated annealing based novel approach for solving multi-objective energy-efficient scheduling on heterogeneous DVS activated processors in high-performance real-time systems. Real-time task scheduling problem is a well-known NP-hard problem. In these systems, tasks are usually associated with deadlines and represented by directed acyclic graphs since they depend on each other. So, system designers face difficulty in finding suitable solutions that can satisfy all the objectives of task scheduling, as warranted for proficient operations of such systems. Hence, this paper introduces a novel algorithm, called archived multi-objective simulated annealing for energy-efficient real-time scheduling (AMOSA-E2RTS) that finds an optimal schedule satisfying the precedence and deadline constraints. In the proposed algorithm, a domination concept leads towards finding the optimal trade-off solutions and tasks are prioritized according to three different policies i.e., latest deadline first (LDF), execution ranking and energy ranking policy. A suitable numerical example is used to demonstrate the working of the proposed approach. Experimental findings suggest that the proposed algorithm is capable of producing energy efficient scheduling decisions which satisfy all related constraints. Statistical analysis of the results has been conducted.","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":"131361210","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.2018.8477846
L. R. Rodrigues, J. Gomes, A. Neto, A. Souza
The Symbiotic Organism Search (SOS) algorithm is an optimization metaheuristic inspired by the symbiotic relationships that occur among organisms in nature. In the last few years, the SOS algorithm attracted increasing attention due to its good performance on various real-world problems, despite the fact that no specific parameter adjustment is required. In this paper, we propose an improved version of SOS by modifying the organisms selection strategy. In the proposed version of the algorithm, three organisms are selected from the population without having a predefined symbiotic relationship. Once the organisms are selected, an assignment step is conducted to assign each organism to a symbiotic relationship. We tested the performance of the proposed algorithm using twenty benchmark instances of the flow shop scheduling problem. We compared the results with the results obtained using the original SOS algorithm. The proposed modification improved the performance of the SOS algorithm in the search for the global optimum value in most of the instances.
{"title":"A Modified Symbiotic Organisms Search Algorithm Applied to Flow Shop Scheduling Problems","authors":"L. R. Rodrigues, J. Gomes, A. Neto, A. Souza","doi":"10.1109/CEC.2018.8477846","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477846","url":null,"abstract":"The Symbiotic Organism Search (SOS) algorithm is an optimization metaheuristic inspired by the symbiotic relationships that occur among organisms in nature. In the last few years, the SOS algorithm attracted increasing attention due to its good performance on various real-world problems, despite the fact that no specific parameter adjustment is required. In this paper, we propose an improved version of SOS by modifying the organisms selection strategy. In the proposed version of the algorithm, three organisms are selected from the population without having a predefined symbiotic relationship. Once the organisms are selected, an assignment step is conducted to assign each organism to a symbiotic relationship. We tested the performance of the proposed algorithm using twenty benchmark instances of the flow shop scheduling problem. We compared the results with the results obtained using the original SOS algorithm. The proposed modification improved the performance of the SOS algorithm in the search for the global optimum value in most of the instances.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"15 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":"121759866","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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