基于多种群分解预测的动态多目标引力搜索算法
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作者单位:

1.东北林业大学;2.东北林业大学机电工程学院;3.东北林业大学工程技术学院

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TP301.6

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Dynamic multi-objective gravitational searching algorithm based on multi-population decomposition prediction
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Northeast Forestry University

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    摘要:

    为提高算法求解动态多目标问题的寻优性能,本文提出一种多种群分解预测动态多目标算法。首先,提出进化向量生成策略,即基于偏好目标的解生成一组均匀分布的平行向量,并采用引力搜索算法优化每个子问题,保证其对应解的精度和分布的均匀性;其次,设计插值生成策略,即根据进化向量子问题的解在目标空间中的取值,通过线性插值的方式生成更多非支配解,保证解集的多样性和均匀性;再次,在环境变化后,根据相邻子问题的解存在相近性预测生成搜索种群,提高算法的寻优速度。与四个对比算法在十个标准动态测试函数进行对比分析,实验结果表明本文算法求解动态多目标问题具有较好的分布性和收敛性。

    Abstract:

    In order to improve the non-dominant solution set with better convergence and distributivity of dynamic multi-objective problems, a multi-population decomposition prediction algorithm is proposed in this paper. Firstly, an evolutionary vector adaptive generation strategy is proposed, which generates a set of uniform evolutionary vectors based on the solutions of preference sub-problems to ensure the convergence and distribution of the Pareto set. The new non-dominant solution is obtained based on the location of the solution of the subproblem in the target space. Thirdly, a predictive model is adopted to initialize the subpopulation to improve the optimum speed and performance of the algorithm. The experimental results show that compared with four existing algorithms, the proposed algorithm has obvious advantages in convergence and distribution over ten standard dynamic multi-objective problems.

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历史
  • 收稿日期:2020-07-20
  • 最后修改日期:2020-09-27
  • 录用日期:2020-10-12
  • 在线发布日期: 2020-12-01
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