During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
针对低地球轨道(low earth orbit,LEO)卫星接入地球同步轨道(geosynchronous earth orbit,GEO)卫星频谱中时存在GEO卫星频谱变化不平稳、非线性的问题,提出了一种基于改进粒子群优化(improved particle swarm optimization,IPSO)图卷积...针对低地球轨道(low earth orbit,LEO)卫星接入地球同步轨道(geosynchronous earth orbit,GEO)卫星频谱中时存在GEO卫星频谱变化不平稳、非线性的问题,提出了一种基于改进粒子群优化(improved particle swarm optimization,IPSO)图卷积网络和长短期记忆(graph convolutional networks and long short term memory,GCN-LSTM)网络的星间频谱预测模型。该模型利用GCN-LSTM网络学习频谱数据的时频域特征,并结合自注意力机制调整关键信息的权重分配;利用基于非线性调整惯性权重策略和柯西变异策略改进后的粒子群算法寻优GCN-LSTM网络的第一层LSTM单元数、第二层LSTM单元数、学习率、随机失活率(dropout)和批处理量(batch_size),进而提高模型的预测准确性。使用采集的高轨卫星频谱数据集,对1 s、30 s和1 min三种频谱预测场景完成实验对比,结果表明:相较于卷积长短期记忆网络(convolutional long short term memory,ConvLSTM)基线模型,本文模型的平均绝对误差(mean absolute error,MAE)分别降低了27%、17.63%、17.68%,具有更好的频谱预测能力。展开更多
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
文摘针对低地球轨道(low earth orbit,LEO)卫星接入地球同步轨道(geosynchronous earth orbit,GEO)卫星频谱中时存在GEO卫星频谱变化不平稳、非线性的问题,提出了一种基于改进粒子群优化(improved particle swarm optimization,IPSO)图卷积网络和长短期记忆(graph convolutional networks and long short term memory,GCN-LSTM)网络的星间频谱预测模型。该模型利用GCN-LSTM网络学习频谱数据的时频域特征,并结合自注意力机制调整关键信息的权重分配;利用基于非线性调整惯性权重策略和柯西变异策略改进后的粒子群算法寻优GCN-LSTM网络的第一层LSTM单元数、第二层LSTM单元数、学习率、随机失活率(dropout)和批处理量(batch_size),进而提高模型的预测准确性。使用采集的高轨卫星频谱数据集,对1 s、30 s和1 min三种频谱预测场景完成实验对比,结果表明:相较于卷积长短期记忆网络(convolutional long short term memory,ConvLSTM)基线模型,本文模型的平均绝对误差(mean absolute error,MAE)分别降低了27%、17.63%、17.68%,具有更好的频谱预测能力。
