To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.Fir...To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.展开更多
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
基金Project(60925011) supported by the National Natural Science Foundation for Distinguished Young Scholars of ChinaProject(9140A06040510BQXXXX) supported by Advanced Research Foundation of General Armament Department,China
文摘To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.
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。
