Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization
The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objectiv...
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| Vydáno v: | IEEE transactions on evolutionary computation Ročník 27; číslo 2; s. 326 - 340 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers' best level in overall performance while achieving great saving in execution time. |
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| AbstractList | The generation and updating of solutions, e.g., crossover and mutation, of many existing evolutionary algorithms directly operate on decision variables. The operators are very time consuming for large-scale and many-objective optimization problems. Different from them, this work proposes an objective space-based population generation method to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions. It introduces three new objective vector generation methods and uses a linear mapping method to tightly connect objective space and decision one to jointly determine new-generation solutions. A loop can be formed directly between two spaces, which can generate new solutions faster and use more feedback information in the objective space. In order to demonstrate the performance of the proposed algorithm, this work performs a series of empirical experiments involving both large-scale decision variables and many objectives. Compared with the state-of-the-art traditional and large-scale algorithms, the proposed method exceeds or at least reaches its peers' best level in overall performance while achieving great saving in execution time. |
| Author | Kang, Qi Zhou, MengChu Zhang, Liang Deng, Qi An, Jing |
| Author_xml | – sequence: 1 givenname: Qi orcidid: 0000-0003-3154-6711 surname: Deng fullname: Deng, Qi email: 1652365@tongji.edu.cn organization: Department of Control Science and Engineering, Tongji University, Shanghai, China – sequence: 2 givenname: Qi orcidid: 0000-0001-7128-6913 surname: Kang fullname: Kang, Qi email: qkang@tongji.edu.cn organization: Department of Control Science and Engineering, Tongji University, Shanghai, China – sequence: 3 givenname: Liang surname: Zhang fullname: Zhang, Liang email: rainbow_zhli@tongji.edu.cn organization: Department of Control Science and Engineering, Tongji University, Shanghai, China – sequence: 4 givenname: MengChu orcidid: 0000-0002-5408-8752 surname: Zhou fullname: Zhou, MengChu email: zhou@njit.edu organization: ECE Department, New Jersey Institute of Technology, Newark, NJ, USA – sequence: 5 givenname: Jing orcidid: 0000-0002-3946-3526 surname: An fullname: An, Jing email: anjing_tj@163.com organization: School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China |
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| SubjectTerms | Approximation algorithms Convergence Decision variables decomposition Evolutionary algorithms Evolutionary computation Genetic algorithms Inverse problems large-scale evolution many-objective evolution Multiple objective analysis objective space mapping Optimization Sociology Statistics |
| Title | Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization |
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