During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable landscapes.However,due to the ...During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable landscapes.However,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision variables.To tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions.The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search space.More importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by GPUs.While existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real variables.Furthermore,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.展开更多
In order to accelerate the sedimentation of super-large-scale argillized ultrafine tailings with bad features such as low settling velocity, muddy overflow water, and large flocculant dosage, a fly-ash-based magnetic ...In order to accelerate the sedimentation of super-large-scale argillized ultrafine tailings with bad features such as low settling velocity, muddy overflow water, and large flocculant dosage, a fly-ash-based magnetic coagulant （FAMC） was used in a dynamic experimental device. To obtain the best possible combination of the impact factors （magnetic intensity, FAMC dosage, flocculant dosage, and feed speed） for minimum overflow turbidity, a response surface methodology test coupled with a four-factor five-level central composite design was conducted. The synergy mechanism of FAMC and flocculant was analyzed based on the potential measurement and scanning electron microscopy. The results show that the flocculant dosage, overflow turbidity, and solid content can be reduced by 50%, 90%, and 80%, while the handling capacity per unit and efficiency of backfill and dry stacking can be promoted by 20%, 17%, and 13%, respectively, with a magnetic intensity of 0.3 T, FAMC dosage of 200 mL/t, flocculant dosage of 30 g/t, and feed speed of 0.6 t/（m^2·h）. Therefore, synergy of FAMC and flocculant has obvious efficiency in saving energy and protecting the environment by allowing 70×10^6 t/a of argillized ultrafine tailings slurry to be disposed safely and efficiently with a cost saving of more than 53×106 Yuan/a, which gives it great promise for use in domestic and foreign mines.展开更多
基金This work was supported in part by the National Key Research and Development Program of China(2018AAA0100100)the National Natural Science Foundation of China(61822301,61876123,61906001)+2 种基金the Collaborative Innovation Program of Universities in Anhui Province(GXXT-2020-051)the Hong Kong Scholars Program(XJ2019035)Anhui Provincial Natural Science Foundation(1908085QF271).
文摘During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable landscapes.However,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision variables.To tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal solutions.The proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search space.More importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by GPUs.While existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real variables.Furthermore,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.
基金Project(2012BAC09B02)supported by the 12th-Five Years Key Programs for Science and Technology Development of ChinaProject(2015zzts078)supported by the Fundamental Research Funds for the Central Universities of Central South University,ChinaProject(2015CX005)supported by Innovation Driven Plan of Central South University,China
文摘In order to accelerate the sedimentation of super-large-scale argillized ultrafine tailings with bad features such as low settling velocity, muddy overflow water, and large flocculant dosage, a fly-ash-based magnetic coagulant （FAMC） was used in a dynamic experimental device. To obtain the best possible combination of the impact factors （magnetic intensity, FAMC dosage, flocculant dosage, and feed speed） for minimum overflow turbidity, a response surface methodology test coupled with a four-factor five-level central composite design was conducted. The synergy mechanism of FAMC and flocculant was analyzed based on the potential measurement and scanning electron microscopy. The results show that the flocculant dosage, overflow turbidity, and solid content can be reduced by 50%, 90%, and 80%, while the handling capacity per unit and efficiency of backfill and dry stacking can be promoted by 20%, 17%, and 13%, respectively, with a magnetic intensity of 0.3 T, FAMC dosage of 200 mL/t, flocculant dosage of 30 g/t, and feed speed of 0.6 t/（m^2·h）. Therefore, synergy of FAMC and flocculant has obvious efficiency in saving energy and protecting the environment by allowing 70×10^6 t/a of argillized ultrafine tailings slurry to be disposed safely and efficiently with a cost saving of more than 53×106 Yuan/a, which gives it great promise for use in domestic and foreign mines.
