System reliability optimization : a fuzzy genetic algorithm approach
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: System reliability optimization , Multi-objective optimization , Genetic algorithm , Fuzzy optimization
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/376308 , uj:4944 , http://hdl.handle.net/10210/13044
- Description: System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising.
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- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2013
- Subjects: System reliability optimization , Multi-objective optimization , Genetic algorithm , Fuzzy optimization
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/376308 , uj:4944 , http://hdl.handle.net/10210/13044
- Description: System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising.
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A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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A new multi-swarm multi-objective particle swarm optimization based on pareto front set
- Sun, Yanxia, Van Wyk, Barend Jacobus, Wang, Zenghui
- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Multiple swarms , Pareto front
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22263 , uj:16181 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2011. A new multi-swarm multi-objective particle swarm optimization based on pareto front set. Lecture Notes in Artificial Intelligence (LNAI) 6839:203-210. ISBN:978-3-642-25944-9
- Description: Abstract: In this paper, a new multi-swarm method is proposed for multiobjective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality. , Originally presented at 2011 International Conference on Intelligent Computing, Zhengzhou, China 11-14 August, 2011.
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- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Multiple swarms , Pareto front
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22263 , uj:16181 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2011. A new multi-swarm multi-objective particle swarm optimization based on pareto front set. Lecture Notes in Artificial Intelligence (LNAI) 6839:203-210. ISBN:978-3-642-25944-9
- Description: Abstract: In this paper, a new multi-swarm method is proposed for multiobjective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality. , Originally presented at 2011 International Conference on Intelligent Computing, Zhengzhou, China 11-14 August, 2011.
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Lexicographic multi-objective optimization of thermoacoustic refrigerator’s stack
- Tartibu, L.K., Sun, B., Kaunda, M.A.E.
- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2015
- Subjects: Thermoacoustic , Stack , Cooling Load , Coefficient of performance , Multi-objective optimization , GAMS
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17624 , uj:15907 , ISSN: 0947-7411 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Lexicographic multi-objective optimisation of thermoacoustic refrigerator’s stack. Journal of Heat and Mass Transfer. 51(5): 649-660. DOI: 10.1007/s00231-014-1440-z. , DOI: 10.1007/s00231-014-1440-z
- Description: Abstract: This work develops a novel mathematical programming model to optimize the performance of a simple thermoacoustic refrigerator (TAR). This study aims to optimize the geometric parameters namely the stack position, the stack length, the blockage ratio and the plate spacing involved in designing TARs. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimization task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLP). Since we optimize multiple objectives simultaneously, each objective component has been given a weighting factor to provide appropriate user-defined emphasis. A practical example is given to illustrate the approach. We have determined a design statement of a stack describing how the geometrical parameters describing would change if emphasis is given to one objective in particular. We also considered optimization of multiple objectives components simultaneously and identify global optimal solutions describing the stack geometry using a lexicographic multiobjective optimization scheme. Additionally, this approach illustrates the difference between a design for maximum cooling and best coefficient of performance of a simple TAR.
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- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2015
- Subjects: Thermoacoustic , Stack , Cooling Load , Coefficient of performance , Multi-objective optimization , GAMS
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17624 , uj:15907 , ISSN: 0947-7411 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Lexicographic multi-objective optimisation of thermoacoustic refrigerator’s stack. Journal of Heat and Mass Transfer. 51(5): 649-660. DOI: 10.1007/s00231-014-1440-z. , DOI: 10.1007/s00231-014-1440-z
- Description: Abstract: This work develops a novel mathematical programming model to optimize the performance of a simple thermoacoustic refrigerator (TAR). This study aims to optimize the geometric parameters namely the stack position, the stack length, the blockage ratio and the plate spacing involved in designing TARs. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimization task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLP). Since we optimize multiple objectives simultaneously, each objective component has been given a weighting factor to provide appropriate user-defined emphasis. A practical example is given to illustrate the approach. We have determined a design statement of a stack describing how the geometrical parameters describing would change if emphasis is given to one objective in particular. We also considered optimization of multiple objectives components simultaneously and identify global optimal solutions describing the stack geometry using a lexicographic multiobjective optimization scheme. Additionally, this approach illustrates the difference between a design for maximum cooling and best coefficient of performance of a simple TAR.
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Thermal losses considerations in thermo-acoustic engine design
- Authors: Tartibu, L.K.
- Date: 2016
- Subjects: Thermo-acoustic engine , Modelling , Multi-objective optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/385373 , http://hdl.handle.net/10210/217031 , uj:21588 , Citation: Tartibu, L.K. 2016. Thermal losses considerations in thermo-acoustic engine design.
- Description: Abstract: Thermo-acoustic cooling as an environmentally friendly refrigeration system is one of the research areas being pursued. Although not commercially available and simple to fabricate, the designing of thermo-acoustic coolers involves significant technical challenges. Many fundamental issues related to the thermo-acoustic effects and the associated heat transfer must be addressed. The most inhibiting characteristic of current thermo-acoustic cooling devices is the lack of efficiency. The stack has been identified as the heart of the device where the heat transfer takes place. Improving its performance will make thermo-acoustic technology more attractive. Most of the existing efforts have not taken thermal losses to the surroundings into account in the derivation of the models. Five different parameters describing the stack geometry and the angular frequency of the standing wave are considered. This work explores the use of a multi-objective optimization approach to model and to optimize the performance of a simple thermo-acoustic engine. The present study highlights the importance of thermal losses in the modelling of small-scale thermo-acoustic engines using a multi-objective approach. The unique characteristic of this research is the computing of all efficient optimal solutions describing the best geometrical configuration of thermo-acoustic engines.
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- Authors: Tartibu, L.K.
- Date: 2016
- Subjects: Thermo-acoustic engine , Modelling , Multi-objective optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/385373 , http://hdl.handle.net/10210/217031 , uj:21588 , Citation: Tartibu, L.K. 2016. Thermal losses considerations in thermo-acoustic engine design.
- Description: Abstract: Thermo-acoustic cooling as an environmentally friendly refrigeration system is one of the research areas being pursued. Although not commercially available and simple to fabricate, the designing of thermo-acoustic coolers involves significant technical challenges. Many fundamental issues related to the thermo-acoustic effects and the associated heat transfer must be addressed. The most inhibiting characteristic of current thermo-acoustic cooling devices is the lack of efficiency. The stack has been identified as the heart of the device where the heat transfer takes place. Improving its performance will make thermo-acoustic technology more attractive. Most of the existing efforts have not taken thermal losses to the surroundings into account in the derivation of the models. Five different parameters describing the stack geometry and the angular frequency of the standing wave are considered. This work explores the use of a multi-objective optimization approach to model and to optimize the performance of a simple thermo-acoustic engine. The present study highlights the importance of thermal losses in the modelling of small-scale thermo-acoustic engines using a multi-objective approach. The unique characteristic of this research is the computing of all efficient optimal solutions describing the best geometrical configuration of thermo-acoustic engines.
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Multi-objective optimization of the stack of a thermoacoustic engine using GAMS
- Tartibu, L.K., Sun, B., Kaunda, M.A.E.
- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2014
- Subjects: Thermoacoustics engine , Multi-objective optimization , GAMS , Mathematical programming
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17579 , uj:15902 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Multi-objective optimisation of a thermoacoustic regenerator using GAMS. Journal of Applied Soft Computing, 28: 30–43.
- Description: Abstract: tThis work illustrates the use of a multi-objective optimization approach to model and optimize theperformance of a simple thermoacoustic engine. System parameters and constraints that capture theunderlying thermoacoustic dynamics have been used to define the model. Work output, viscous loss,conductive heat loss, convective heat loss and radiative heat loss have been used to measure the per-formance of the engine. The optimization task is formulated as a five-criterion mixed-integer non-linearprogramming problem. Since we optimize multiple objectives simultaneously, each objective componenthas been given a weighting factor to provide appropriate user-defined emphasis. A practical example isgiven to illustrate the approach. We have determined a design statement of a stack describing how thedesign would change if emphasis is given to one objective in particular. We also considered optimiza-tion of multiple objectives components simultaneously and identify global optimal solutions describingthe stack geometry using the augmented ε-constraint method. This approach has been implemented inGAMS (General Algebraic Modelling System).
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- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2014
- Subjects: Thermoacoustics engine , Multi-objective optimization , GAMS , Mathematical programming
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17579 , uj:15902 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Multi-objective optimisation of a thermoacoustic regenerator using GAMS. Journal of Applied Soft Computing, 28: 30–43.
- Description: Abstract: tThis work illustrates the use of a multi-objective optimization approach to model and optimize theperformance of a simple thermoacoustic engine. System parameters and constraints that capture theunderlying thermoacoustic dynamics have been used to define the model. Work output, viscous loss,conductive heat loss, convective heat loss and radiative heat loss have been used to measure the per-formance of the engine. The optimization task is formulated as a five-criterion mixed-integer non-linearprogramming problem. Since we optimize multiple objectives simultaneously, each objective componenthas been given a weighting factor to provide appropriate user-defined emphasis. A practical example isgiven to illustrate the approach. We have determined a design statement of a stack describing how thedesign would change if emphasis is given to one objective in particular. We also considered optimiza-tion of multiple objectives components simultaneously and identify global optimal solutions describingthe stack geometry using the augmented ε-constraint method. This approach has been implemented inGAMS (General Algebraic Modelling System).
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Optimal design of a standing wave thermoacoustic refrigerator using GAMS
- Tartibu, L.K., Sun, B., Kaunda, M.A.E.
- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2015
- Subjects: Thermoacoustic refrigerator , Coefficient of Performance , Cooling , Multi-objective optimization , GAMS
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/17707 , uj:15915 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Optimal design of a standing wave thermoacoustic refrigerator using GAMS. Procedia Computer Science, 62, 611-618. The 2015 International Conference on Soft Computing and Software Engineering, 5-6 March, 2015, University of California, Berkeley.
- Description: Abstract: This work proposes a multi-objective optimization approach to model and optimize small scale standing wave thermoacoustic refrigerator (TAR). This study aims to optimize the geometric variables namely the stack position, the stack length, the blockage ratio and the plate spacing involved in designing thermoacoustic refrigerators. Unlike most previous studies, these variables are considered interdependent. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimization task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLP). A practical example considering three different gases is given to illustrate the approach. This approach has been implemented in the software GAMS (General Algebraic modelling System) and Pareto optimal solutions describing the most preferred geometry for maximum performance of the device are computed using the augmented -constraint method.
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- Authors: Tartibu, L.K. , Sun, B. , Kaunda, M.A.E.
- Date: 2015
- Subjects: Thermoacoustic refrigerator , Coefficient of Performance , Cooling , Multi-objective optimization , GAMS
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/17707 , uj:15915 , Citation: Tartibu, L.K., Sun, B. & Kaunda M.A.E. 2015. Optimal design of a standing wave thermoacoustic refrigerator using GAMS. Procedia Computer Science, 62, 611-618. The 2015 International Conference on Soft Computing and Software Engineering, 5-6 March, 2015, University of California, Berkeley.
- Description: Abstract: This work proposes a multi-objective optimization approach to model and optimize small scale standing wave thermoacoustic refrigerator (TAR). This study aims to optimize the geometric variables namely the stack position, the stack length, the blockage ratio and the plate spacing involved in designing thermoacoustic refrigerators. Unlike most previous studies, these variables are considered interdependent. System parameters and constraints that capture the underlying thermoacoustic dynamics have been used to define the models. The cooling load, the coefficient of performance and the acoustic power loss have been used to measure the performance of the device. The optimization task is formulated as a three-criterion nonlinear programming problem with discontinuous derivatives (DNLP). A practical example considering three different gases is given to illustrate the approach. This approach has been implemented in the software GAMS (General Algebraic modelling System) and Pareto optimal solutions describing the most preferred geometry for maximum performance of the device are computed using the augmented -constraint method.
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Fully connected multi-objective particle swarm optimizer based on neural network
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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