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| 自适应的多目标模拟退火优化算法(英文) | |
| 引用本文： | 啜钢,赵丹,孙礼.自适应的多目标模拟退火优化算法(英文)[J].中国通信学报,2012,9(9):68-78. |
| 作者姓名： | 啜钢 赵丹 孙礼 |
| 作者单位： | School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,P.R.China;Key Laboratory of Universal Wireless Communication,Ministry of Education,Beijing University of Posts and Telecommunications,Beijing 100876,P.R.China;Beijing Key Laboratory of Network System Architecture and Convergence,Beijing University of Posts and Telecommunications,Beijing 100876,P.R.China |
| 基金项目： | supported by the Major National Science & Technology Specific Project of China under Grants No.2010ZX03002-007-02,No.2009ZX03002-002,No.2010ZX03002-002-03 |
| 摘 要： | In recent years,simulated annealing algorithms have been extensively developed and utilized to solve multi-objective optimization problems.In order to obtain better optimization performance,this paper proposes a Novel Adaptive Simulated Annealing (NASA) algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing (AMOSA).For handling multi-objective,NASA makes improvements in three aspects:sub-iteration search,sub-archive and adaptive search,which effectively strengthen the stability and efficiency of the algorithm.For handling constraints,NASA introduces corresponding solution acceptance criterion.Furthermore,NASA has also been applied to optimize TD-LTE network performance by adjusting antenna parameters;it can achieve better extension and convergence than AMOSA,NSGAII and MOPSO.Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimization performance. |
| 关 键 词： | simulated annealing constrained multi-objective optimization adaptive sub-iteration searching sub-archive pareto-optimal |
| 收稿时间： | 2012-10-25; |
Novel Adaptive Simulated Annealing Algorithm for Constrained Multi-Objective Optimization |
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| Chuai Gang,Zhao Dan,Sun Li.Novel Adaptive Simulated Annealing Algorithm for Constrained Multi-Objective Optimization[J].China communications magazine,2012,9(9):68-78. | |
| Authors: | Chuai Gang Zhao Dan Sun Li |
| Affiliation: | 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China 2Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China 3Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China |
| Abstract: | In recent years, simulated annealing algorithms have been extensively developed and utilized to solve multi-objective optimization problems. In order to obtain better optimization performance, this paper proposes a Novel Adaptive Simulated Annealing (NASA) algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing ( AMOSA ). For handling multi-objective, NASA makes improvements in three aspects: sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithm. For handling constraints, NASA introduces corresponding solution acceptance criterion. Furthermore, NASA has also been applied to optimize TD-LTE network performance by adjusting antenna parameters; it can achieve better extension and convergence than AMOSA, NSGAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimization performance. |
| Keywords: | simulated annealing constrained multi-objective optimization adaptive sub-iteration searching sub-archive pareto-optimal |
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