Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network
In this paper, the application of three well-known multi-objective optimization algorithms to water distribution network (WDN) optimum design has been considered. Non-dominated sorting genetic algorithm II (NSGA-II), Multi-objective differential evolution (MODE) and Multi-objective particle swarm op...
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| Veröffentlicht in: | Ain Shams Engineering Journal Jg. 10; H. 1; S. 103 - 111 |
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| Sprache: | Englisch |
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Elsevier B.V
01.03.2019
Elsevier |
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| ISSN: | 2090-4479 |
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| Abstract | In this paper, the application of three well-known multi-objective optimization algorithms to water distribution network (WDN) optimum design has been considered. Non-dominated sorting genetic algorithm II (NSGA-II), Multi-objective differential evolution (MODE) and Multi-objective particle swarm optimization (MOPSO) algorithms are applied to benchmark mathematical test function problems for evaluating the performance of these algorithms. The Accuracy and computational runtime are the two indicators used for the comparison of these three algorithms. The optimization results of mathematical test functions show that all three algorithms were able to accurately produce Pareto Front, but the computational time of MODE algorithm to achieve the optimal solutions is lower than the two other algorithms. Then, the discussed algorithms have been used to optimize the WDN design problem. Comparison of the generated solutions on the Pareto Front for WDN design shows that the obtained Pareto Front of MODE is more accurate and faster. |
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| AbstractList | In this paper, the application of three well-known multi-objective optimization algorithms to water distribution network (WDN) optimum design has been considered. Non-dominated sorting genetic algorithm II (NSGA-II), Multi-objective differential evolution (MODE) and Multi-objective particle swarm optimization (MOPSO) algorithms are applied to benchmark mathematical test function problems for evaluating the performance of these algorithms. The Accuracy and computational runtime are the two indicators used for the comparison of these three algorithms. The optimization results of mathematical test functions show that all three algorithms were able to accurately produce Pareto Front, but the computational time of MODE algorithm to achieve the optimal solutions is lower than the two other algorithms. Then, the discussed algorithms have been used to optimize the WDN design problem. Comparison of the generated solutions on the Pareto Front for WDN design shows that the obtained Pareto Front of MODE is more accurate and faster. In this paper, the application of three well-known multi-objective optimization algorithms to water distribution network (WDN) optimum design has been considered. Non-dominated sorting genetic algorithm II (NSGA-II), Multi-objective differential evolution (MODE) and Multi-objective particle swarm optimization (MOPSO) algorithms are applied to benchmark mathematical test function problems for evaluating the performance of these algorithms. The Accuracy and computational runtime are the two indicators used for the comparison of these three algorithms. The optimization results of mathematical test functions show that all three algorithms were able to accurately produce Pareto Front, but the computational time of MODE algorithm to achieve the optimal solutions is lower than the two other algorithms. Then, the discussed algorithms have been used to optimize the WDN design problem. Comparison of the generated solutions on the Pareto Front for WDN design shows that the obtained Pareto Front of MODE is more accurate and faster. Keywords: Multi-objective optimization, Genetic algorithm, Differential evolution, Particle swarm, Water distribution design |
| Author | Jamali, A. Farmani, R. Monsef, H. Naghashzadegan, M. |
| Author_xml | – sequence: 1 givenname: H. surname: Monsef fullname: Monsef, H. email: monsef@phd.guilan.ac.ir organization: University of Guilan, Department of Mechanical Engineering, Rasht, Iran – sequence: 2 givenname: M. surname: Naghashzadegan fullname: Naghashzadegan, M. email: naghash@guilan.ac.ir organization: University of Guilan, Department of Mechanical Engineering, Rasht, Iran – sequence: 3 givenname: A. surname: Jamali fullname: Jamali, A. email: ali.jamali@guilan.ac.ir organization: University of Guilan, Department of Mechanical Engineering, Rasht, Iran – sequence: 4 givenname: R. surname: Farmani fullname: Farmani, R. email: R.Farmani@exeter.ac.uk organization: University of Exeter, Exeter, Devon, UK |
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| Keywords | Differential evolution Multi-objective optimization Water distribution design Particle swarm Genetic algorithm |
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| SubjectTerms | Differential evolution Genetic algorithm Multi-objective optimization Particle swarm Water distribution design |
| Title | Comparison of evolutionary multi objective optimization algorithms in optimum design of water distribution network |
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