A Dual-System Variable-Grain Cooperative Coevolutionary Algorithm: Satellite-Module Layout Design
The layout design of complex engineering systems (such as satellite-module layout design) is very difficult to solve in polynomial time. This is not only a complex coupled system design problem but also a special combinatorial problem. The fitness function for this problem is characterized as multim...
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| Vydáno v: | IEEE transactions on evolutionary computation Ročník 14; číslo 3; s. 438 - 455 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.06.2010
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | The layout design of complex engineering systems (such as satellite-module layout design) is very difficult to solve in polynomial time. This is not only a complex coupled system design problem but also a special combinatorial problem. The fitness function for this problem is characterized as multimodal because of interference constraints among layout components (objects), etc. This characteristic can easily result in premature convergence when solving this problem using evolutionary algorithms. To deal with the above two problems simultaneously, we propose a dual-system framework based on the cooperative coevolutionary algorithm (CCEA, e.g., cooperative coevolutionary genetic algorithm) like multidisciplinary design optimization. The proposed algorithm has the characteristic of solving the complex coupled system problem, increasing the diversity of population, and decreasing the premature convergence. The basis for the proposed algorithm is as follows. The original coupled system P is decomposed into several subsystems according to its physical structure. The system P is duplicated as systems A and B, respectively. The A system is solved on a global level (all-in-one), whereas the solving of B system is realized through the computation of its subsystems in parallel. The individual migration between A and B is implemented through the individual migration between their corresponding subsystems. To reduce the computational complexity produced additionally by the dual-systems A and B, we employ a variable-grain model of design variables. During the process of optimization, the two systems A and B gradually approximate to the original system P, respectively. The above-proposed algorithm is called the dual-system variable-grain cooperative coevolution algorithm (DVGCCEA) or Oboe-CCEA. The numerical experimental results of a simplified satellite-module layout design case show that the proposed algorithm can obtain better robustness and trade-off between computational precision and computational efficiency. |
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| AbstractList | The layout design of complex engineering systems (such as satellite-module layout design) is very difficult to solve in polynomial time. This is not only a complex coupled system design problem but also a special combinatorial problem. The fitness function for this problem is characterized as multimodal because of interference constraints among layout components (objects), etc. This characteristic can easily result in premature convergence when solving this problem using evolutionary algorithms. To deal with the above two problems simultaneously, we propose a dual-system framework based on the cooperative coevolutionary algorithm (CCEA, e.g., cooperative coevolutionary genetic algorithm) like multidisciplinary design optimization. The proposed algorithm has the characteristic of solving the complex coupled system problem, increasing the diversity of population, and decreasing the premature convergence. The basis for the proposed algorithm is as follows. The original coupled system P is decomposed into several subsystems according to its physical structure. The system P is duplicated as systems A and B, respectively. The A system is solved on a global level (all-in-one), whereas the solving of B system is realized through the computation of its subsystems in parallel. The individual migration between A and B is implemented through the individual migration between their corresponding subsystems. To reduce the computational complexity produced additionally by the dual-systems A and B, we employ a variable-grain model of design variables. During the process of optimization, the two systems A and B gradually approximate to the original system P, respectively. The above-proposed algorithm is called the dual-system variable-grain cooperative coevolution algorithm (DVGCCEA) or Oboe-CCEA. The numerical experimental results of a simplified satellite-module layout design case show that the proposed algorithm can obtain better robustness and trade-off between computational precision and computational efficiency. |
| Author | Hong-fei Teng Wei Zeng Yan-jun Shi Yu Chen Qing-hua Hu |
| Author_xml | – sequence: 1 givenname: Hong-Fei surname: TENG fullname: TENG, Hong-Fei organization: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China – sequence: 2 surname: YU CHEN fullname: YU CHEN organization: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China – sequence: 3 surname: WEI ZENG fullname: WEI ZENG organization: China North Vehicle Research Institute, Beijing 100072, China – sequence: 4 givenname: Yan-Jun surname: SHI fullname: SHI, Yan-Jun organization: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China – sequence: 5 givenname: Qing-Hua surname: HU fullname: HU, Qing-Hua organization: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China |
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| Keywords | Combinatorial problem Evolutionary algorithm Complex system Satellite Fork join problem Combinatorial optimization Modeling premature convergence Systems engineering Cooperation Robustness Multidisciplinary Divide and conquer method Physical structure variable-grain Process planning satellite-module layout Dual-system coevolutionary Coevolution Computational complexity Polynomial time Engineering design Genetic algorithm system layout design Parallelization |
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| SubjectTerms | Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Combinatorial analysis Computation Computer science; control theory; systems Concurrent computing Convergence Design engineering Design optimization Dual-system coevolutionary Evolutionary algorithms Evolutionary computation Exact sciences and technology Genetic algorithms Heuristic Interference constraints Learning and adaptive systems Mathematical analysis Mathematical models Mechanical engineering. Machine design Polynomials premature convergence satellite-module layout Satellites Studies system layout design Systems engineering and theory Theoretical computing variable-grain |
| Title | A Dual-System Variable-Grain Cooperative Coevolutionary Algorithm: Satellite-Module Layout Design |
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