在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最...在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最优帕累托前沿也是一件非常有挑战性的工作。已有的鲁棒多目标进化算法能够比较好地处理低维鲁棒多目标优化问题,即问题的决策变量维数不超过10,但对于高维鲁棒多目标优化问题的表现往往不好。提出了一种结合自编码器以及协同进化方法的多目标进化算法(Decomposition-based Multiobjective Evolutionary Algorithm Assisted by Autoencoder and Cooperative Coevolution,MOEA/D-AECC),用来解决可降维的高维鲁棒多目标优化问题。该算法利用两个不同种群分别优化原始多目标优化问题以及对应的鲁棒多目标优化问题。为提高算法处理高维问题的能力,该算法利用自编码器模型对高维数据进行降维,从而提取出高维数据的低维特征。通过重构这些低维特征来学习可靠的下降方向,之后沿着可靠的下降方向采样产生新解。最后,通过实验测试了MOEA/D-AECC算法在一组可降维的高维鲁棒多目标优化问题上的表现。实验结果表明,MOEA/D-AECC算法的寻优显著优于其他几种代表性的鲁棒多目标进化算法。展开更多
This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels fro...This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.展开更多
The multiple knapsack problem （MKP） forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms （GAs） for proving its efficiency. GAs use self- ...The multiple knapsack problem （MKP） forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms （GAs） for proving its efficiency. GAs use self- adaptability to effectively solve complex problems with constraints, but in certain cases, self-adaptability fails by converging toward an infeasible region. This pitfall can be resolved by using different existing repairing techniques; however, this cannot assure convergence toward attaining the optimal solution. To overcome this issue, gene position-based suppression （GPS） has been modeled and embedded as a new phase in a classical GA. This phase works on the genes of a newly generated individual after the recombination phase to retain the solution vector within its feasible region and to im- prove the solution vector to attain the optimal solution. Genes holding the highest expressibility are reserved into a subset, as the best genes identified from the current individuals by re- placing the weaker genes from the subset. This subset is used by the next generated individual to improve the solution vec- tor and to retain the best genes of the individuals. Each gene＇s positional point and its genotype exposure for each region in an environment are used to fit the best unique genes. Further, suppression of expression in conflicting gene＇s relies on the requirement toward the level of exposure in the environment or in eliminating the duplicate genes from the environment.The MKP benchmark instances from the OR-library are taken for the experiment to test the new model. The outcome por- trays that GPS in a classical GA is superior in most of the cases compared to the other existing repairing techniques.展开更多
文摘在很多实际应用问题中,不确定性的存在对于优化问题的最优解的性能会产生影响。在求解不确定环境下的优化问题时,往往需要考虑解的鲁棒性。最优解的鲁棒性定义通常要考虑其局部邻域内所有解的表现。在多目标优化背景下,如何逼近鲁棒最优帕累托前沿也是一件非常有挑战性的工作。已有的鲁棒多目标进化算法能够比较好地处理低维鲁棒多目标优化问题,即问题的决策变量维数不超过10,但对于高维鲁棒多目标优化问题的表现往往不好。提出了一种结合自编码器以及协同进化方法的多目标进化算法(Decomposition-based Multiobjective Evolutionary Algorithm Assisted by Autoencoder and Cooperative Coevolution,MOEA/D-AECC),用来解决可降维的高维鲁棒多目标优化问题。该算法利用两个不同种群分别优化原始多目标优化问题以及对应的鲁棒多目标优化问题。为提高算法处理高维问题的能力,该算法利用自编码器模型对高维数据进行降维,从而提取出高维数据的低维特征。通过重构这些低维特征来学习可靠的下降方向,之后沿着可靠的下降方向采样产生新解。最后,通过实验测试了MOEA/D-AECC算法在一组可降维的高维鲁棒多目标优化问题上的表现。实验结果表明,MOEA/D-AECC算法的寻优显著优于其他几种代表性的鲁棒多目标进化算法。
基金Supported by the Fundamental Research Funds for the Central Universities in North China Electric Power University(11MG13)the Natural Science Foundation of Hebei Province(F2011502038)
文摘This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.
文摘The multiple knapsack problem （MKP） forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms （GAs） for proving its efficiency. GAs use self- adaptability to effectively solve complex problems with constraints, but in certain cases, self-adaptability fails by converging toward an infeasible region. This pitfall can be resolved by using different existing repairing techniques; however, this cannot assure convergence toward attaining the optimal solution. To overcome this issue, gene position-based suppression （GPS） has been modeled and embedded as a new phase in a classical GA. This phase works on the genes of a newly generated individual after the recombination phase to retain the solution vector within its feasible region and to im- prove the solution vector to attain the optimal solution. Genes holding the highest expressibility are reserved into a subset, as the best genes identified from the current individuals by re- placing the weaker genes from the subset. This subset is used by the next generated individual to improve the solution vec- tor and to retain the best genes of the individuals. Each gene＇s positional point and its genotype exposure for each region in an environment are used to fit the best unique genes. Further, suppression of expression in conflicting gene＇s relies on the requirement toward the level of exposure in the environment or in eliminating the duplicate genes from the environment.The MKP benchmark instances from the OR-library are taken for the experiment to test the new model. The outcome por- trays that GPS in a classical GA is superior in most of the cases compared to the other existing repairing techniques.
