Adaptive population-based increased learning algorithm and its application in optimization

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (6): 225-227.

• 工程与应用 • Previous Articles Next Articles

Adaptive population-based increased learning algorithm and its application in optimization

WANG Lihua¹，MA Liangli¹，SHI Xiangrong²

1.Department of Computer Engineering，Naval University of Engineering，Wuhan 430033，China
2.92815 Units，PLA

Received:1900-01-01 Revised:1900-01-01 Online:2011-02-21 Published:2011-02-21

应用于组合优化的自适应PBIL算法研究

汪丽华¹，马良荔¹，石向荣²

1.海军工程大学计算机工程系，武汉 430033
2.中国人民解放军92815部队

Abstract

Abstract: For solving the problem of searching the best solution in the processes of optimization，a new Population-Based Increased Learning（PBIL） algorithm is put forward.With the introduce of system entropy，learning percent and optimum capability of the traditional PBIL algorithm can be adjusted automatically.And that is the adaptive PBIL algorithm which has adaptive function and mutation capability.When it is used in optimization problem，it makes the processes of the optimization more effective.

Key words: optimization, adaptivel, Population-Based Increased Learning（PBIL） algorithm

摘要： 为解决组合优化过程中最优解的搜索效率问题，研究了一种基于自适应理论的PBIL算法。通过引入系统熵值，使传统PBIL算法的学习概率和变异率能根据系统熵值的变化作自适应调整，形成具有自学习和变异能力的自适应PBIL算法（APBIL）。通过实例验证了该算法的实用价值和有效性。

关键词: 组合优化, 自适应, 基于人口的增量学习（PBIL）算法

WANG Lihua¹，MA Liangli¹，SHI Xiangrong². Adaptive population-based increased learning algorithm and its application in optimization[J]. Computer Engineering and Applications, 2011, 47(6): 225-227.

汪丽华¹，马良荔¹，石向荣². 应用于组合优化的自适应PBIL算法研究[J]. 计算机工程与应用, 2011, 47(6): 225-227.

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Adaptive population-based increased learning algorithm and its application in optimization

应用于组合优化的自适应PBIL算法研究

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