Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHIS...Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.展开更多
Error accumulation in long-term predictions of chaotic climate systems is caused primarily by the model's high sensitivity to initial conditions and the absence of dynamic adjustment mechanisms,leading to gradual ...Error accumulation in long-term predictions of chaotic climate systems is caused primarily by the model's high sensitivity to initial conditions and the absence of dynamic adjustment mechanisms,leading to gradual forecast divergence.This presents a critical challenge to achieving stable long-term predictions.While current data-driven approaches perform well in short-term forecasting,their accuracy deteriorates significantly over time.To overcome this limitation,we propose an autonomous echo state network with a snow ablation optimizer(AESN-SAO),which significantly improves the adaptability and robustness of data-driven methods under varying initial conditions.This approach not only eliminates the need for manual hyperparameter tuning in traditional AESNs but also effectively mitigates the common issue of initial conditions sensitivity in chaotic climate systems.Furthermore,we introduce a sparse observation insertion mechanism based on the Lyapunov time and valid prediction time(VPT),which enables AESNSAO to correct errors prior to system divergence,effectively extending the prediction horizon.Numerical experiments conducted on the Lorenz-63 and Climate Lorenz-63 systems demonstrate that integrating sparse observations with AESN-SAO approach extends the VPT to approximately 99 Lyapunov times,markedly reducing error accumulation in long-term forecasts.This study provides a reliable and efficient framework for long-term predictions in climate systems with nonlinear and chaotic dynamics,with promising applications in weather forecasting,climate modeling,and disaster risk assessment.展开更多
基金supported by the Key Research and Development Program of Henan Province under grant Nos.241111222900 and 241111210400the Natural Science Foundation of Henan under grant No.222300420583+3 种基金the Key Research Projects of Henan Higher Education Institutions under grant No.22A590003the National Natural Science Foundation of China under Grant 62103379the Key Science and Technology Program of Henan Province under grant No.232102220067the Maker Space Incubation Project under Grant No.2023ZCKJ102.
文摘Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer(SAO),a Physically Hybrid strategy-based Improved Snow Ablation Optimizer(PHISAO)is proposed.In this paper,a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity.Secondly,the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy,which is supplemented with a position update formula for the water evaporation phase.Additionally,Cauchy mutation perturbation has been introduced in the snow melting phase.This set of improvements better balances the exploration and exploitation phases of the algorithm,enhancing its ability to pursue excellence.Finally,a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation,helping the algorithm to escape from the local optimum.Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC(Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites.The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness.In addition,the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization,beluga whale optimization,sand cat swarm optimization,and SAO.The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps.The proposed PHISAO objective function values were reduced by an average of 29.49%(map 1),and 18.34%(map 2)compared to SAO.
基金supported by the National Natural Science Foundation of China(Grant Nos.42430112,42371377)。
文摘Error accumulation in long-term predictions of chaotic climate systems is caused primarily by the model's high sensitivity to initial conditions and the absence of dynamic adjustment mechanisms,leading to gradual forecast divergence.This presents a critical challenge to achieving stable long-term predictions.While current data-driven approaches perform well in short-term forecasting,their accuracy deteriorates significantly over time.To overcome this limitation,we propose an autonomous echo state network with a snow ablation optimizer(AESN-SAO),which significantly improves the adaptability and robustness of data-driven methods under varying initial conditions.This approach not only eliminates the need for manual hyperparameter tuning in traditional AESNs but also effectively mitigates the common issue of initial conditions sensitivity in chaotic climate systems.Furthermore,we introduce a sparse observation insertion mechanism based on the Lyapunov time and valid prediction time(VPT),which enables AESNSAO to correct errors prior to system divergence,effectively extending the prediction horizon.Numerical experiments conducted on the Lorenz-63 and Climate Lorenz-63 systems demonstrate that integrating sparse observations with AESN-SAO approach extends the VPT to approximately 99 Lyapunov times,markedly reducing error accumulation in long-term forecasts.This study provides a reliable and efficient framework for long-term predictions in climate systems with nonlinear and chaotic dynamics,with promising applications in weather forecasting,climate modeling,and disaster risk assessment.
