Measuring and comparing sustainable development levels of cities,regions,and projects are an essential procedure in creating and maintaining sustainable urban futures.Introducing a new urban sustainable development as...Measuring and comparing sustainable development levels of cities,regions,and projects are an essential procedure in creating and maintaining sustainable urban futures.Introducing a new urban sustainable development assessment model:The Live and Work(LaW)City Model.As an indicator-based sustainable development indexing model,it aims to assist planners and policy makers in their decisionmaking procedures and development of cities by providing an integrated assessment framework.Several efforts were done to develop composite indicators,but it was concluded that most of them focused on environmental dimensions and neglected the overlapping of the other influential ones.The paper gives an overview of the nature and importance of composite indicators and how it can be structured and implemented precisely;overcoming its drawbacks,to gauge and rank the performance of cities across the demands of people,planet,and profit,as a way of achieving the LaW balance criteria.The methodology of the model is developed by following a set of logical steps such as weighing,normalization,and aggregation of individual indicators.From here,a functional form for aggregation was derived to compute the index.Furthermore,the structure of the model was illustrated in the form of a pie/radar chart.The visualized LaW city index has the communicative advantage of being easy to convey comparative levels of different values.The total values computed could be used to rank cities in a tabular format and gauge their comparable sustainable development performance accordingly.The model can also estimate the sustainable development outcomes of alternative development scenarios during different year’s intervals.This can be achieved by stacking up different model scenarios;making it a relatively simple exercise for both the general public and decision makers to comprehend.展开更多
Portfolio optimization is a classical and important problem in the field of asset management,which aims to achieve a trade-off between profit and risk.Previous portfolio optimization models use traditional risk measur...Portfolio optimization is a classical and important problem in the field of asset management,which aims to achieve a trade-off between profit and risk.Previous portfolio optimization models use traditional risk measurements such as variance,which symmetrically delineate both positive and negative sides and are not practical and stable.In this paper,a new model with cardinality constraints is first proposed,in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way.The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms(MOEAs).To solve the model,a Learning-Guided Evolutionary Algorithm based on I_(ϵ+)indicator(I_(ϵ+)LGEA)is developed.In I_(ϵ+)LGEA,the I_(ϵ+)indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm.And a new constraint-handling method based on I_(ϵ+)indicator is also adopted to ensure the feasibility of solutions.The experimental results on five portfolio trading datasets including up to 1226 assets show that I_(ϵ+)LGEA outperforms some state-of-the-art MOEAs in most cases.展开更多
文摘Measuring and comparing sustainable development levels of cities,regions,and projects are an essential procedure in creating and maintaining sustainable urban futures.Introducing a new urban sustainable development assessment model:The Live and Work(LaW)City Model.As an indicator-based sustainable development indexing model,it aims to assist planners and policy makers in their decisionmaking procedures and development of cities by providing an integrated assessment framework.Several efforts were done to develop composite indicators,but it was concluded that most of them focused on environmental dimensions and neglected the overlapping of the other influential ones.The paper gives an overview of the nature and importance of composite indicators and how it can be structured and implemented precisely;overcoming its drawbacks,to gauge and rank the performance of cities across the demands of people,planet,and profit,as a way of achieving the LaW balance criteria.The methodology of the model is developed by following a set of logical steps such as weighing,normalization,and aggregation of individual indicators.From here,a functional form for aggregation was derived to compute the index.Furthermore,the structure of the model was illustrated in the form of a pie/radar chart.The visualized LaW city index has the communicative advantage of being easy to convey comparative levels of different values.The total values computed could be used to rank cities in a tabular format and gauge their comparable sustainable development performance accordingly.The model can also estimate the sustainable development outcomes of alternative development scenarios during different year’s intervals.This can be achieved by stacking up different model scenarios;making it a relatively simple exercise for both the general public and decision makers to comprehend.
基金This work was supported by the National Natural Science Foundation of China(Nos.62173258 and 61773296).
文摘Portfolio optimization is a classical and important problem in the field of asset management,which aims to achieve a trade-off between profit and risk.Previous portfolio optimization models use traditional risk measurements such as variance,which symmetrically delineate both positive and negative sides and are not practical and stable.In this paper,a new model with cardinality constraints is first proposed,in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way.The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms(MOEAs).To solve the model,a Learning-Guided Evolutionary Algorithm based on I_(ϵ+)indicator(I_(ϵ+)LGEA)is developed.In I_(ϵ+)LGEA,the I_(ϵ+)indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm.And a new constraint-handling method based on I_(ϵ+)indicator is also adopted to ensure the feasibility of solutions.The experimental results on five portfolio trading datasets including up to 1226 assets show that I_(ϵ+)LGEA outperforms some state-of-the-art MOEAs in most cases.
