Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms a...Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of LSMOPs.By contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for LSMOPs.Currently,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored.In this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method.On the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front.On the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent.In comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.展开更多
This paper presents both application and comparison of the metaheuristic techniques to multi-area economic dispatch(MAED)problem with tie line constraints considering transmission losses,multiple fuels,valve-point loa...This paper presents both application and comparison of the metaheuristic techniques to multi-area economic dispatch(MAED)problem with tie line constraints considering transmission losses,multiple fuels,valve-point loading and prohibited operating zones.The metaheuristic techniques such as differential evolution,evolutionary programming,genetic algorithm and simulated annealing are applied to solve MAED problem.These metaheuristic techniques for MAED problem are evaluated on three different test systems,both small and large,involving varying degree of complexity and the results are compared against each other.展开更多
People completely lacking body fat(lipodystrophy/lipoatrophy)and those with severe obesity both show profound metabolic and other health issues.Regulating levels of body fat somewhere between these limits would,theref...People completely lacking body fat(lipodystrophy/lipoatrophy)and those with severe obesity both show profound metabolic and other health issues.Regulating levels of body fat somewhere between these limits would,therefore,appear to be adaptive.Two different models might be contemplated.More traditional is a set point(SP)where the levels are regulated around a fixed level.Alternatively,dual-intervention point(DIP)is a system that tolerates fairly wide variation but is activated when critically high or low levels are breached.The DIP system seems to fit our experience much better than an SP,and models suggest that it is more likely to have evolved.A DIP system may have evolved because of two contrasting selection pressures.At the lower end,we may have been selected to avoid low levels of fat as a buffer against starvation,to avoid disease-induced anorexia,and to support reproduction.At the upper end,we may have been selected to avoid excess storage because of the elevated risks of predation.This upper limit of control seems to have malfunctioned because some of us deposit large fat stores,with important negative health effects.Why has evolution not protected us against this problem?One possibility is that the protective system slowly fell apart due to random mutations after we dramatically reduced the risk of being predated during our evolutionary history.By chance,it fell apart more in some people than others,and these people are now unable to effectively manage their weight in the face of the modern food glut.To understand the evolutionary context of obesity,it is important to separate the adaptive reason for storing some fat(i.e.the lower intervention point),from the nonadaptive reason for storing lots of fat(a broken upper intervention point).The DIP model has several consequences,showing how we understand the obesity problem and what happens when we attempt to treat it.展开更多
基金supported in part by the National Key Research and Development Program of China(2018AAA0100100)the National Natural Science Foundation of China(61906001,62136008,U21A20512)+1 种基金the Key Program of Natural Science Project of Educational Commission of Anhui Province(KJ2020A0036)Alexander von Humboldt Professorship for Artificial Intelligence Funded by the Federal Ministry of Education and Research,Germany。
文摘Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of LSMOPs.By contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for LSMOPs.Currently,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored.In this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method.On the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front.On the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent.In comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.
文摘This paper presents both application and comparison of the metaheuristic techniques to multi-area economic dispatch(MAED)problem with tie line constraints considering transmission losses,multiple fuels,valve-point loading and prohibited operating zones.The metaheuristic techniques such as differential evolution,evolutionary programming,genetic algorithm and simulated annealing are applied to solve MAED problem.These metaheuristic techniques for MAED problem are evaluated on three different test systems,both small and large,involving varying degree of complexity and the results are compared against each other.
基金This work was supported by the Shenzhen Key Laboratory of Metabolic Health(ZDSYS20210427152400001)to JRSthe US National Institutes of Health grants R01DK100659,R01DK118725,P01DK119130 and R01DK12724 to JKE.
文摘People completely lacking body fat(lipodystrophy/lipoatrophy)and those with severe obesity both show profound metabolic and other health issues.Regulating levels of body fat somewhere between these limits would,therefore,appear to be adaptive.Two different models might be contemplated.More traditional is a set point(SP)where the levels are regulated around a fixed level.Alternatively,dual-intervention point(DIP)is a system that tolerates fairly wide variation but is activated when critically high or low levels are breached.The DIP system seems to fit our experience much better than an SP,and models suggest that it is more likely to have evolved.A DIP system may have evolved because of two contrasting selection pressures.At the lower end,we may have been selected to avoid low levels of fat as a buffer against starvation,to avoid disease-induced anorexia,and to support reproduction.At the upper end,we may have been selected to avoid excess storage because of the elevated risks of predation.This upper limit of control seems to have malfunctioned because some of us deposit large fat stores,with important negative health effects.Why has evolution not protected us against this problem?One possibility is that the protective system slowly fell apart due to random mutations after we dramatically reduced the risk of being predated during our evolutionary history.By chance,it fell apart more in some people than others,and these people are now unable to effectively manage their weight in the face of the modern food glut.To understand the evolutionary context of obesity,it is important to separate the adaptive reason for storing some fat(i.e.the lower intervention point),from the nonadaptive reason for storing lots of fat(a broken upper intervention point).The DIP model has several consequences,showing how we understand the obesity problem and what happens when we attempt to treat it.
