基于逐層演化的群體智能算法優化

張水平; 王碧; 陳陽

doi:10.13374/j.issn2095-9389.2017.03.020

基于逐層演化的群體智能算法優化

doi: 10.13374/j.issn2095-9389.2017.03.020

張水平¹,
王碧^1, ,,
陳陽¹

江西理工大學信息工程學院, 贛州 341000

詳細信息

中圖分類號: TP301.6
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- 文章訪問數: 876
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- 被引次數: 0
出版歷程
- 收稿日期: 2016-05-09

Optimization for swarm intelligence based on layer-by-layer evolution

Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

摘要

摘要: 為能徹底解決群體智能算法早熟問題的同時保持原算法主體不變且可與現有優化理論協同優化，在前期仿真實驗和理論證明的基礎上，提出了一種逐層演化的改進策略.利用在原算法中構建基于搜索空間壓縮理論的自適應系統，通過逐層的壓縮、選擇、再初始化的操作，以包括壓縮后搜索空間在內的社會信息作為遺傳知識，指導尋優過程，從而實現最終解精度的提升、避免早熟問題的出現.對基準函數進行仿真實驗可以看出該策略在提升算法精度，增強后期個體活性方面具有良好的表現.
- 群體智能 /
- 搜索空間 /
- 逐層演化 /
- 早熟
Abstract: A layer-by-layer evolution strategy was proposed to deal with the premature convergence of swarm intelligence as a collaborator with other existing researches based on pre-experiments and simple proofs. For promoting the precision of solution and eviting the premature convergence, the self-adaption system was constructed on the basis of the primal algorithm, operations such as compression, selection and re-initialization using the technology of layer-by-layer, and the social information was used including the compressed research space and the optimal solution. The improvements of precision of solution and the vitality of terminal individuals can be found in results of simulation experiments with benchmark functions.
- swarm intelligence /
- search space /
- layer-by-layer /
- premature convergence

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參考文獻(19)

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