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基于信息熵的改进PESA算法

王堃1，王琳琳1，刘艳2，张玉华1，吴蒙1   

  1. 1. 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室，江苏 南京 210003； 2. 南京工业大学 自动化与电气工程学院，江苏 南京 210009
  • 出版日期:2013-11-25 发布日期:2013-11-15
  • 基金资助:
    国家重点基础研究发展计划(“973”计划)基金资助项目(2011CB302903)；国家自然科学基金资助项目（61100199, 61100213, 61170276, 61201160）；教育部高等学校博士学科点专项科研基金资助项目（20113223120007）；江苏省高校自然科学研究基金资助项目（10KJA510035, 12KJD510007）；南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金资助项目（NYKL201107）

Improved PESA algorithm based on comentropy

  • Online:2013-11-25 Published:2013-11-15

摘要： 针对PESA算法所需的计算运算量、计算难度及运算时间都随着解集数量的增加而急剧增加的问题，将熵值度量指标引入到PESA算法中，提出了基于信息熵的PESA算法(C-PESA, comentropy-based PESA)。该算法根据信息熵指标在量化度量Pareto解集的分布特性，判断种群进化是否到达成熟阶段，本算法迭代1 300次时即到达成熟阶段，从而尽早结束了算法复杂的优化过程，在一定程度上简化了PESA算法的时间复杂度。仿真结果表明，随着进化种群数量的增长，C-PESA算法的计算量只是呈现线性增加，算法的计算时间缩短接近4倍，进化计算效率得到提高。

Abstract: Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merging the entropy value metric into PESA algorithm was proposed. According to the distributed characteristic of the entropy value metric over the Pareto solution set, the proposed algorithm could determine whether the population has developed to the mature stage, which is reached when the number of iterations is 1 300 in C-PESA. Thereby, the optimization process can be finished as soon as possible, and in a certain extent, the time complexity of PESA was simplified. Simulation results show that the computational effort of C-PESA increases linearly with the rising number of solutions. Meanwhile, the computation time is improved almost four times, and the evolutionary computation efficiency is also enhanced.

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