Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experi...Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.展开更多
Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are unc...Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are uncertain.Despite recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked systems.In this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities.We discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed systems.We validated our approach using a 20-year dataset from an eelgrass restoration project.Counterintuitively,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase biodiversity.Our computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.展开更多
Bio-inspired computing(BIC),short for biologically inspired computing,is a field of study that loosely knits together subfields related to the topics of connectionism,social behaviour and emergence.The field of bio-in...Bio-inspired computing(BIC),short for biologically inspired computing,is a field of study that loosely knits together subfields related to the topics of connectionism,social behaviour and emergence.The field of bio-inspired computing brings together researchers from many disciplines,including biology,computer science,mathematics,physics and genetics.展开更多
It is proved that for given integer k≥2, almost all k-hypertournaments are strong and in almost all k-hypertournaments, every pair of vertices lies on a 3-cycle.
基金supported by the National Natural Science Foundation of China(U20A20306,62276191)the Fundamental Research Funds for the Central Universities(HUST2023JYCXJJ011).
文摘Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.
基金supported by the National Key Research and Development Program of China(2022ZD0119601)the National Natural Science Foundation of China(62225306 and U2141235)+3 种基金the Guangdong Basic and Applied Research Foundation(2022B1515120069)the support of the US National Science Foundation(2047488)the Rensselaer–IBM Artificial Intelligence Research Collaborationthe support of China Scholarship Council(202206160043)。
文摘Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are uncertain.Despite recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked systems.In this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities.We discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed systems.We validated our approach using a 20-year dataset from an eelgrass restoration project.Counterintuitively,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase biodiversity.Our computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.
文摘Bio-inspired computing(BIC),short for biologically inspired computing,is a field of study that loosely knits together subfields related to the topics of connectionism,social behaviour and emergence.The field of bio-inspired computing brings together researchers from many disciplines,including biology,computer science,mathematics,physics and genetics.
文摘It is proved that for given integer k≥2, almost all k-hypertournaments are strong and in almost all k-hypertournaments, every pair of vertices lies on a 3-cycle.
