The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process o...The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.展开更多
为了面对具有不确定性的新能源大规模接入电网给传统电力市场带来的挑战,文中针对传统电力市场出清策略时间尺度长、新型交易主体收益低等问题,提出考虑风光不确定性的风光火储联合发电系统参与电力现货市场和调频辅助服务市场联合交易...为了面对具有不确定性的新能源大规模接入电网给传统电力市场带来的挑战,文中针对传统电力市场出清策略时间尺度长、新型交易主体收益低等问题,提出考虑风光不确定性的风光火储联合发电系统参与电力现货市场和调频辅助服务市场联合交易的出清策略。首先通过拉丁超立方抽样和Kantorovich距离削减对样本场景进行处理,生成典型的风光出力场景;然后以生成的典型风光出力场景为研究对象,提出以最小化发电成本为目标的风光火储联合发电系统参与电力现货市场和调频辅助服务市场联合交易的出清策略,并采用交替方向乘子法(alternating direction method of multipliers,ADMM)进行求解;最后在IEEE 39节点系统中搭建相应的数学模型,采用西北某地风光数据为算例进行仿真验证。结果表明,文中所提策略可以合理分配能源出力,提高能源利用率和各交易主体的收益。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
基金support of projects of Ministry of Education of Czech Republic KONTAKT No.LH12062previous achievements worked out under the project of Technological Agency of Czech Republic No.TA01011019.
文摘The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.
文摘为了面对具有不确定性的新能源大规模接入电网给传统电力市场带来的挑战,文中针对传统电力市场出清策略时间尺度长、新型交易主体收益低等问题,提出考虑风光不确定性的风光火储联合发电系统参与电力现货市场和调频辅助服务市场联合交易的出清策略。首先通过拉丁超立方抽样和Kantorovich距离削减对样本场景进行处理,生成典型的风光出力场景;然后以生成的典型风光出力场景为研究对象,提出以最小化发电成本为目标的风光火储联合发电系统参与电力现货市场和调频辅助服务市场联合交易的出清策略,并采用交替方向乘子法(alternating direction method of multipliers,ADMM)进行求解;最后在IEEE 39节点系统中搭建相应的数学模型,采用西北某地风光数据为算例进行仿真验证。结果表明,文中所提策略可以合理分配能源出力,提高能源利用率和各交易主体的收益。
