为了提高机场货运区(Elevating Transfer Vehicle,ETV)转运效率,建立以最小化任务集调度时间为优化目标的调度模型,提出一种混合的粒子群算法对ETV调度问题求解。算法对加速因子采取动态的自适应调整策略;采用混沌序列替代标准粒子群中...为了提高机场货运区(Elevating Transfer Vehicle,ETV)转运效率,建立以最小化任务集调度时间为优化目标的调度模型,提出一种混合的粒子群算法对ETV调度问题求解。算法对加速因子采取动态的自适应调整策略;采用混沌序列替代标准粒子群中的随机数;建立平均粒距、适应度方差和汉明距离相结合的早熟判断机制并采用混沌算子扰动微粒的位置来跳出局部最优。通过实例验证和遗传算法、模拟退火等经典的优化算法以及非线性学习因子粒子群、混沌粒子群等改进的粒子群算法相比,该算法在ETV调度最优序列的求解中收敛速度快,全局寻优能力强,稳定性好;和传统的链式调度算法相比,平均调度任务时间减少了15.6%,较好地解决了ETV转运效率低的问题。展开更多
Many questions in natural science and engineering can be transformed into nonlinear equations. Newton iteration method is an important technique to one dimensional and multidimensional variables and iteration itself i...Many questions in natural science and engineering can be transformed into nonlinear equations. Newton iteration method is an important technique to one dimensional and multidimensional variables and iteration itself is very sensitive to initial guess point. This sensitive area is the Julia set of nonlinear discrete dynamic system which Newton iteration method forms. The Julia set, which is the boundaries of basins of attractions, displays the intricate fractal structures and chaos phenomena. By constructing repulsion two-cycle point function and making use of inverse image iteration method, a method to find Julia set point was introduced. For the first time, a new method to find all solutions was proposed based on utilizing sensitive fractal areas to locate the Julia set points to find all solutions of the nonlinear questions. The developed technique used an important feature of fractals to preserve shape of basins of attraction on infinitely small scales. The numerical examples in linkage synthesis showed that the method was effective and correct.展开更多
The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algo...The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.展开更多
文摘为了提高机场货运区(Elevating Transfer Vehicle,ETV)转运效率,建立以最小化任务集调度时间为优化目标的调度模型,提出一种混合的粒子群算法对ETV调度问题求解。算法对加速因子采取动态的自适应调整策略;采用混沌序列替代标准粒子群中的随机数;建立平均粒距、适应度方差和汉明距离相结合的早熟判断机制并采用混沌算子扰动微粒的位置来跳出局部最优。通过实例验证和遗传算法、模拟退火等经典的优化算法以及非线性学习因子粒子群、混沌粒子群等改进的粒子群算法相比,该算法在ETV调度最优序列的求解中收敛速度快,全局寻优能力强,稳定性好;和传统的链式调度算法相比,平均调度任务时间减少了15.6%,较好地解决了ETV转运效率低的问题。
基金Sponsored by the Scientific Research Fund of Ministry Education(Grant No.02108),and the Key Scientific Research Fund of Hunan Provincial Education Depart-ment(Grant No.04A036),and the Grant of the11-th Five-year Plan for Key Construction Disciplines Mechanical Design and Theory of Hunan Province.
文摘Many questions in natural science and engineering can be transformed into nonlinear equations. Newton iteration method is an important technique to one dimensional and multidimensional variables and iteration itself is very sensitive to initial guess point. This sensitive area is the Julia set of nonlinear discrete dynamic system which Newton iteration method forms. The Julia set, which is the boundaries of basins of attractions, displays the intricate fractal structures and chaos phenomena. By constructing repulsion two-cycle point function and making use of inverse image iteration method, a method to find Julia set point was introduced. For the first time, a new method to find all solutions was proposed based on utilizing sensitive fractal areas to locate the Julia set points to find all solutions of the nonlinear questions. The developed technique used an important feature of fractals to preserve shape of basins of attraction on infinitely small scales. The numerical examples in linkage synthesis showed that the method was effective and correct.
基金The National Natural Science Foundation of China(Grant No.81973791)funded this research.
文摘The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.
