A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of hu...A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm＇s performance is studied using a challenging set of typically complex functions with comparison of differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.展开更多
According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flo...According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment.展开更多
Because of the range-angle dependency in random log frequency diverse array(RD-log-FDA) radar, a method for designing beamspace transformation matrix in angle and range based on the receive signal has been proposed.It...Because of the range-angle dependency in random log frequency diverse array(RD-log-FDA) radar, a method for designing beamspace transformation matrix in angle and range based on the receive signal has been proposed.It is demonstrated that the designed beamspace transformation matrix basically meets the requirements of beam gain.However, there are some problems in the transformation matrix designed, such as unstable beam gain and high sidelobe.Hence, we propose an optimization method by adjusting array element spacing and random number in frequency offset to get the optimum beam gain.Therefore, particle swarm optimization(PSO) is used to find the optimal solution.The beam gain comparison before and after the optimization is obtained by simulation, and the results show that the optimized array after beamspace preprocessing has more stable beam gain, lower sidelobe, and higher resolution in parameter estimation.In conclusion, the RD-log-FDA is capable of forming desired beam gain in angle and distance through beamspace preprocessing, and suppressing interference signals in other areas.展开更多
准确掌握森林覆盖空间分布对于森林生态系统保护、恢复和可持续利用至关重要。但高效、精准地获取县域尺度复杂森林覆盖变化依靠低空间分辨率遥感影像结合传统计算机分类模型已经无法满足。以黑龙江省佳木斯汤原县复杂森林为研究对象,...准确掌握森林覆盖空间分布对于森林生态系统保护、恢复和可持续利用至关重要。但高效、精准地获取县域尺度复杂森林覆盖变化依靠低空间分辨率遥感影像结合传统计算机分类模型已经无法满足。以黑龙江省佳木斯汤原县复杂森林为研究对象,采用哨兵一号、二号(Sentinel-1、Sentinel-2)中空间分辨率卫星遥感影像,构建基于粒子群优化算法(particle swarm optimization,PSO)优化的机器学习模型,检测县域尺度森林覆盖变化,应用K-折交叉验证对检测森林覆盖结果进行精度评价。研究结果表明,基于粒子群算法优化的支持向量机和随机森林2个机器学习模型与未经参数优化的自身模型相比,森林覆盖变化检测精度均得到提高,支持向量机模型提高6.52%,随机森林模型提高4.65%。与目前主流ESA World Cover土地覆盖产品相比,基于粒子群算法优化的随机森林模型精度最高,总体精度达到0.92。优化后的随机森林模型对森林覆盖变化检测也更加精细。通过粒子群优化算法的随机森林模型对中空间分辨率遥感影像进行分类,可以快速、准确地掌握县域尺度森林覆盖空间分布情况,为森林生态系统保护、恢复和可持续利用提供数据和技术支撑。展开更多
基金Supported by National-Natural Science Found for Distinguished Young Scholars of China （61025015）, the Foundation for Innovative Research Groups of National Natural Science Foundation of China （61321003） and the China Scholarship Council
基金supported by the Doctor Students Innovation Foundation of Southwest Jiaotong University.
文摘A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm＇s performance is studied using a challenging set of typically complex functions with comparison of differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.
基金Project(50911130366) supported by the National Natural Science Foundation of China
文摘According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment.
基金supported by the National Natural Science Foundation of China (62001506)。
文摘Because of the range-angle dependency in random log frequency diverse array(RD-log-FDA) radar, a method for designing beamspace transformation matrix in angle and range based on the receive signal has been proposed.It is demonstrated that the designed beamspace transformation matrix basically meets the requirements of beam gain.However, there are some problems in the transformation matrix designed, such as unstable beam gain and high sidelobe.Hence, we propose an optimization method by adjusting array element spacing and random number in frequency offset to get the optimum beam gain.Therefore, particle swarm optimization(PSO) is used to find the optimal solution.The beam gain comparison before and after the optimization is obtained by simulation, and the results show that the optimized array after beamspace preprocessing has more stable beam gain, lower sidelobe, and higher resolution in parameter estimation.In conclusion, the RD-log-FDA is capable of forming desired beam gain in angle and distance through beamspace preprocessing, and suppressing interference signals in other areas.
文摘准确掌握森林覆盖空间分布对于森林生态系统保护、恢复和可持续利用至关重要。但高效、精准地获取县域尺度复杂森林覆盖变化依靠低空间分辨率遥感影像结合传统计算机分类模型已经无法满足。以黑龙江省佳木斯汤原县复杂森林为研究对象,采用哨兵一号、二号(Sentinel-1、Sentinel-2)中空间分辨率卫星遥感影像,构建基于粒子群优化算法(particle swarm optimization,PSO)优化的机器学习模型,检测县域尺度森林覆盖变化,应用K-折交叉验证对检测森林覆盖结果进行精度评价。研究结果表明,基于粒子群算法优化的支持向量机和随机森林2个机器学习模型与未经参数优化的自身模型相比,森林覆盖变化检测精度均得到提高,支持向量机模型提高6.52%,随机森林模型提高4.65%。与目前主流ESA World Cover土地覆盖产品相比,基于粒子群算法优化的随机森林模型精度最高,总体精度达到0.92。优化后的随机森林模型对森林覆盖变化检测也更加精细。通过粒子群优化算法的随机森林模型对中空间分辨率遥感影像进行分类,可以快速、准确地掌握县域尺度森林覆盖空间分布情况,为森林生态系统保护、恢复和可持续利用提供数据和技术支撑。
