Lifecycle-Based Swarm Optimization Method for Numerical Optimization
Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, grow...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2014/892914 |
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| _version_ | 1832551511853367296 |
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| author | Hai Shen Yunlong Zhu Xiaodan Liang |
| author_facet | Hai Shen Yunlong Zhu Xiaodan Liang |
| author_sort | Hai Shen |
| collection | DOAJ |
| description | Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques. |
| format | Article |
| id | doaj-art-0dcbb384831748a68099123ba9165e1d |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0dcbb384831748a68099123ba9165e1d2025-02-03T06:01:09ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/892914892914Lifecycle-Based Swarm Optimization Method for Numerical OptimizationHai Shen0Yunlong Zhu1Xiaodan Liang2College of Physics Science and Technology, Shenyang Normal University, Shenyang 110023, ChinaLaboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang, Shenyang 110016, ChinaSchool of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, ChinaBioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.http://dx.doi.org/10.1155/2014/892914 |
| spellingShingle | Hai Shen Yunlong Zhu Xiaodan Liang Lifecycle-Based Swarm Optimization Method for Numerical Optimization Discrete Dynamics in Nature and Society |
| title | Lifecycle-Based Swarm Optimization Method for Numerical Optimization |
| title_full | Lifecycle-Based Swarm Optimization Method for Numerical Optimization |
| title_fullStr | Lifecycle-Based Swarm Optimization Method for Numerical Optimization |
| title_full_unstemmed | Lifecycle-Based Swarm Optimization Method for Numerical Optimization |
| title_short | Lifecycle-Based Swarm Optimization Method for Numerical Optimization |
| title_sort | lifecycle based swarm optimization method for numerical optimization |
| url | http://dx.doi.org/10.1155/2014/892914 |
| work_keys_str_mv | AT haishen lifecyclebasedswarmoptimizationmethodfornumericaloptimization AT yunlongzhu lifecyclebasedswarmoptimizationmethodfornumericaloptimization AT xiaodanliang lifecyclebasedswarmoptimizationmethodfornumericaloptimization |