Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586315
S. Ling, H. Nguyen, Kit Yan Chan
Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities.
{"title":"Genetic algorithm based fuzzy multiple regression for the nocturnal Hypoglycaemia detection","authors":"S. Ling, H. Nguyen, Kit Yan Chan","doi":"10.1109/CEC.2010.5586315","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586315","url":null,"abstract":"Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"129 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82514428","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586415
A. Mohais, M. Ibrahimov, S. Schellenberg, Neal Wagner, Z. Michalewicz
When an evolutionary algorithm is used as an optimizer in a scheduling software application that is destined for use in a real-world commercial setting, a number of time-variability issues are encountered. This paper explores several such issues and other practical problems that arose during the solution of a scheduling application in the area of wine bottling. Each hurdle was addressed by appropriately adjusting the candidate individual representation, the procedure used to decode an individual, or the objective function itself. Addressing these issues is critical when designing and constructing the evolutionary algorithm, in order to ensure that the resulting system is robust enough to meet the demands of day-to-day use. The approach described in this paper has been proven by implementation and vigorous sustained use in a complex business environment.
{"title":"Time-varying constraints and other practical problems in real-world scheduling applications","authors":"A. Mohais, M. Ibrahimov, S. Schellenberg, Neal Wagner, Z. Michalewicz","doi":"10.1109/CEC.2010.5586415","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586415","url":null,"abstract":"When an evolutionary algorithm is used as an optimizer in a scheduling software application that is destined for use in a real-world commercial setting, a number of time-variability issues are encountered. This paper explores several such issues and other practical problems that arose during the solution of a scheduling application in the area of wine bottling. Each hurdle was addressed by appropriately adjusting the candidate individual representation, the procedure used to decode an individual, or the objective function itself. Addressing these issues is critical when designing and constructing the evolutionary algorithm, in order to ensure that the resulting system is robust enough to meet the demands of day-to-day use. The approach described in this paper has been proven by implementation and vigorous sustained use in a complex business environment.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"3 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76243827","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586413
L. Rachmawati, D. Srinivasan
Preference-based techniques in multi-objective evolutionary algorithms (MOEA) are gaining importance. This paper presents a method of representing, eliciting and integrating decision making preference expressed as a set of imprecise goal vectors into a MOEA with steady-state replacement. The specification of a precise goal vector without extensive knowledge of problem behavior often leads to undesirable results. The approach proposed in this paper facilitates the linguistic specification of goal vectors relative to extreme, non-dominated solutions (i.e. the goal is specified as ”Very Small”, ”Small”, ”Medium”, ”Large”, and ”Very Large”) with three degrees of imprecision as desired by the decision maker. The degree of imprecision corresponds to the density of solutions desired within the target subset. Empirical investigations of the proposed method yield promising results.
{"title":"Incorporation of imprecise goal vectors into evolutionary multi-objective optimization","authors":"L. Rachmawati, D. Srinivasan","doi":"10.1109/CEC.2010.5586413","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586413","url":null,"abstract":"Preference-based techniques in multi-objective evolutionary algorithms (MOEA) are gaining importance. This paper presents a method of representing, eliciting and integrating decision making preference expressed as a set of imprecise goal vectors into a MOEA with steady-state replacement. The specification of a precise goal vector without extensive knowledge of problem behavior often leads to undesirable results. The approach proposed in this paper facilitates the linguistic specification of goal vectors relative to extreme, non-dominated solutions (i.e. the goal is specified as ”Very Small”, ”Small”, ”Medium”, ”Large”, and ”Very Large”) with three degrees of imprecision as desired by the decision maker. The degree of imprecision corresponds to the density of solutions desired within the target subset. Empirical investigations of the proposed method yield promising results.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"68 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87642569","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 : 2010-07-18DOI: 10.1109/CEC.2010.5585986
K. Smith‐Miles, R. Islam
The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository [1], to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.
{"title":"Meta-learning for data summarization based on instance selection method","authors":"K. Smith‐Miles, R. Islam","doi":"10.1109/CEC.2010.5585986","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585986","url":null,"abstract":"The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository [1], to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"5 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87031218","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586501
Sunith Bandaru, K. Deb
Real world multi-objective optimization problems are often solved with the only intention of selecting a single trade-off solution by taking up a decision-making task. The computational effort and time spent on obtaining the entire Pareto front is thus not justifiable. The Pareto solutions as a whole contain within them a lot more information than that is used. Extracting this knowledge would not only give designers a better understanding of the system, but also bring worth to the resources spent. The obtained knowledge acts as governing principles which can help solve other similar systems easily. We propose a genetic algorithm based unsupervised approach for learning these principles from the Pareto-optimal dataset of the base problem. The methodology is capable of discovering analytical relationships of a certain type between different problem entities.
{"title":"Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design","authors":"Sunith Bandaru, K. Deb","doi":"10.1109/CEC.2010.5586501","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586501","url":null,"abstract":"Real world multi-objective optimization problems are often solved with the only intention of selecting a single trade-off solution by taking up a decision-making task. The computational effort and time spent on obtaining the entire Pareto front is thus not justifiable. The Pareto solutions as a whole contain within them a lot more information than that is used. Extracting this knowledge would not only give designers a better understanding of the system, but also bring worth to the resources spent. The obtained knowledge acts as governing principles which can help solve other similar systems easily. We propose a genetic algorithm based unsupervised approach for learning these principles from the Pareto-optimal dataset of the base problem. The methodology is capable of discovering analytical relationships of a certain type between different problem entities.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"35 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87082819","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586000
V. Mariani, Vagner Jorge Neckel, L. D. Afonso, L. Coelho
In this paper a Modified Differential Evolution (MDE) is proposed and its performance for solving the inverse heat transfer problem is compared with Genetic Algorithm with Floating-point representation (GAF) and classical Differential Evolution (DE). The inverse analysis of heat transfer has some practical applications, for example, the estimation of radioactive and thermal properties, such as the conductivity of material with and without the temperatures dependence of diffusive processes. The inverse problems are usually formulated as optimization problems and the main objective becomes the minimization of a cost function. MDE adapts a concept originally proposed in particle swarm optimization design for the dynamic adaptation of mutation factor. Using a piecewise function for apparent thermal conductivity as a function of the temperature data, the heat transfer equation is able to estimate the unknown variables of the inverse problem. The variables that provide the beast least squares fit between the experimental and predicted time-temperatures curves were obtained. Numerical results for inverse heat transfer problem demonstrated the applicability and efficiency of the MDE algorithm. In this application, MDE approach outperforms the GAF and DE best solutions.
{"title":"Differential evolution with dynamic adaptation of mutation factor applied to inverse heat transfer problem","authors":"V. Mariani, Vagner Jorge Neckel, L. D. Afonso, L. Coelho","doi":"10.1109/CEC.2010.5586000","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586000","url":null,"abstract":"In this paper a Modified Differential Evolution (MDE) is proposed and its performance for solving the inverse heat transfer problem is compared with Genetic Algorithm with Floating-point representation (GAF) and classical Differential Evolution (DE). The inverse analysis of heat transfer has some practical applications, for example, the estimation of radioactive and thermal properties, such as the conductivity of material with and without the temperatures dependence of diffusive processes. The inverse problems are usually formulated as optimization problems and the main objective becomes the minimization of a cost function. MDE adapts a concept originally proposed in particle swarm optimization design for the dynamic adaptation of mutation factor. Using a piecewise function for apparent thermal conductivity as a function of the temperature data, the heat transfer equation is able to estimate the unknown variables of the inverse problem. The variables that provide the beast least squares fit between the experimental and predicted time-temperatures curves were obtained. Numerical results for inverse heat transfer problem demonstrated the applicability and efficiency of the MDE algorithm. In this application, MDE approach outperforms the GAF and DE best solutions.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"58 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87643413","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586469
Vui Ann Shim, K. Tan, J. Y. Chia
Estimation of distribution algorithms are increasingly gaining research interest due to their linkage information exploration feature. Two main mechanisms which contribute towards the success of the algorithms are probabilistic modeling and sampling method. Recent attention has been directed towards the development of probabilistic building technique. However, research on the sampling approach is less developed. Thus, this paper carries out an investigation on sampling technique for a novel multi-objective estimation of distribution algorithm — multi-objective restricted Boltzmann machine. Two variants of a new sampling technique based on energy value of the solutions in the trained network are proposed to improve the efficiency of the algorithm. Probabilistic information which is usually clamped into marginal probability distribution may hinder the algorithm in producing solutions that have high linkage dependency between variables. The proposed approach will overcome this limitation of probabilistic modeling in restricted Boltzmann machine. The empirical investigation shows that the proposed algorithm gives promising result in term of convergence and convergence rate.
{"title":"An investigation on sampling technique for multi-objective restricted Boltzmann machine","authors":"Vui Ann Shim, K. Tan, J. Y. Chia","doi":"10.1109/CEC.2010.5586469","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586469","url":null,"abstract":"Estimation of distribution algorithms are increasingly gaining research interest due to their linkage information exploration feature. Two main mechanisms which contribute towards the success of the algorithms are probabilistic modeling and sampling method. Recent attention has been directed towards the development of probabilistic building technique. However, research on the sampling approach is less developed. Thus, this paper carries out an investigation on sampling technique for a novel multi-objective estimation of distribution algorithm — multi-objective restricted Boltzmann machine. Two variants of a new sampling technique based on energy value of the solutions in the trained network are proposed to improve the efficiency of the algorithm. Probabilistic information which is usually clamped into marginal probability distribution may hinder the algorithm in producing solutions that have high linkage dependency between variables. The proposed approach will overcome this limitation of probabilistic modeling in restricted Boltzmann machine. The empirical investigation shows that the proposed algorithm gives promising result in term of convergence and convergence rate.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"5 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87901028","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586543
K. Deb, Rituparna Datta
Evolutionary algorithms are modified in various ways to solve constrained optimization problems. Of them, the use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective, has received a significant attention. Classical penalty function approach is another common methodology which requires an appropriate knowledge of the associated penalty parameter. In this paper, we combine a bi-objective evolutionary approach with the penalty function methodology in a manner complementary to each other. The bi-objective optimization approach provides a good estimate of the penalty parameter, while the unconstrained penalty function approach using classical means provides the overall hybrid algorithm its convergence property. We demonstrate the working of the procedure on a two-variable problem and then solve a number of standard numerical test problems from the EA literature. In all cases, our proposed hybrid methodology is observed to take one or more orders of magnitude smaller number of function evaluations to find the constrained minimum solution accurately. To the best of our knowledge, no previous evolutionary constrained optimization algorithm has reported such a fast and accurate performance on the chosen problems.
{"title":"A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach","authors":"K. Deb, Rituparna Datta","doi":"10.1109/CEC.2010.5586543","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586543","url":null,"abstract":"Evolutionary algorithms are modified in various ways to solve constrained optimization problems. Of them, the use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective, has received a significant attention. Classical penalty function approach is another common methodology which requires an appropriate knowledge of the associated penalty parameter. In this paper, we combine a bi-objective evolutionary approach with the penalty function methodology in a manner complementary to each other. The bi-objective optimization approach provides a good estimate of the penalty parameter, while the unconstrained penalty function approach using classical means provides the overall hybrid algorithm its convergence property. We demonstrate the working of the procedure on a two-variable problem and then solve a number of standard numerical test problems from the EA literature. In all cases, our proposed hybrid methodology is observed to take one or more orders of magnitude smaller number of function evaluations to find the constrained minimum solution accurately. To the best of our knowledge, no previous evolutionary constrained optimization algorithm has reported such a fast and accurate performance on the chosen problems.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"128 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88100582","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586457
S. White, T. Martinez, G. Rudolph
Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Results establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. RP has also been used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.
{"title":"Generating a novel sort algorithm using Reinforcement Programming","authors":"S. White, T. Martinez, G. Rudolph","doi":"10.1109/CEC.2010.5586457","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586457","url":null,"abstract":"Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Results establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. RP has also been used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"2016 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86126256","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586394
G. Yen, W. Leong
In this paper, the proposed constrained multiobejctive particle swarm optimization (MOPSO) adopts the multiobjective constraint handling framework and includes the following design features: An infeasible global best archive to guide the infeasible particles towards feasible region(s); procedures to update the personal best archive are designed to encourage finding feasible regions and convergence towards the Pareto front; acceleration constants in the particle swarm optimization equation are adjusted during the search process to encourage finding more feasible particles or to search for better solutions; and mutation operators are adopted to encourage global and local searches. The simulation results indicate that the proposed algorithm is highly competitive in solving the benchmark problems.
{"title":"Constraint handling procedure for multiobjective particle swarm optimization","authors":"G. Yen, W. Leong","doi":"10.1109/CEC.2010.5586394","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586394","url":null,"abstract":"In this paper, the proposed constrained multiobejctive particle swarm optimization (MOPSO) adopts the multiobjective constraint handling framework and includes the following design features: An infeasible global best archive to guide the infeasible particles towards feasible region(s); procedures to update the personal best archive are designed to encourage finding feasible regions and convergence towards the Pareto front; acceleration constants in the particle swarm optimization equation are adjusted during the search process to encourage finding more feasible particles or to search for better solutions; and mutation operators are adopted to encourage global and local searches. The simulation results indicate that the proposed algorithm is highly competitive in solving the benchmark problems.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"56 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86101246","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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