Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order t...Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.展开更多
A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and Genetic Algorithm:the ratio of lobe wavelength to height(η)and...A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and Genetic Algorithm:the ratio of lobe wavelength to height(η)and the rise angle(α)are selected as the design parameters,and the mixing efficiency,thrust and total pressure loss are the optimization objectives.The CFX commercial solver coupled with the SST turbulence model is employed to simulate the flow field of lobed mixer.A tetrahedral unstructured grid with 5.6 million cells can achieve the similar global results.Based on the response surface approximation model of the lobed mixer,it is necessary to avoid increasing or decreasingαandηat the same time.Instead,theαshould be reduced while theηis appropriately increased,which is conducive to achieving the goal of increasing thrust and reducing losses at the expense of a small decrease in the mixing efficiency.Compared with the normalized method,the non-normalized method with better global optimization accuracy is more suitable for solving the multi-objective optimization problem of the lobed mixer,and its optimal solution(α=8.54°,η=1.165)is the optimal solution of the lobed mixer optimization problem studied in this paper.Compared with the reference lobed mixer,theα,β(the fall angle)and H(lobe height)of the optimal solution are reduced by 0.14°,1.34°and 3.97 mm,respectively,and theηis increased by 0.074;its mixing efficiency is decreased by 4.46%,but the thrust is increased by 2.29%and the total pressure loss is decreased by 0.64%.Downstream of the optimized lobed mixer,the radial scale and peak vorticity of the streamwise voritices decrease with the decreasing lobe height,thereby reducing the mixing efficiency.For the optimized lobed mixer,its low mixing efficiency is the main factor for the decrease of the total pressure loss,but the improvement of the geometric curvature is also conducive to reducing its profile loss.Within the scope of this study,the lobed mixer has an optimal mixing efficiency(ε=74.14%)that maximizes its thrust without excessively increasing the mixing loss.展开更多
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.展开更多
文摘Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.
基金funded by the National Science and Technology Major Project(Grant No.J2019-II-0007-0027)。
文摘A Sequential Approximate Optimization framework(SAO)for the multi-objective optimization of lobed mixer is established by using the BP neural network and Genetic Algorithm:the ratio of lobe wavelength to height(η)and the rise angle(α)are selected as the design parameters,and the mixing efficiency,thrust and total pressure loss are the optimization objectives.The CFX commercial solver coupled with the SST turbulence model is employed to simulate the flow field of lobed mixer.A tetrahedral unstructured grid with 5.6 million cells can achieve the similar global results.Based on the response surface approximation model of the lobed mixer,it is necessary to avoid increasing or decreasingαandηat the same time.Instead,theαshould be reduced while theηis appropriately increased,which is conducive to achieving the goal of increasing thrust and reducing losses at the expense of a small decrease in the mixing efficiency.Compared with the normalized method,the non-normalized method with better global optimization accuracy is more suitable for solving the multi-objective optimization problem of the lobed mixer,and its optimal solution(α=8.54°,η=1.165)is the optimal solution of the lobed mixer optimization problem studied in this paper.Compared with the reference lobed mixer,theα,β(the fall angle)and H(lobe height)of the optimal solution are reduced by 0.14°,1.34°and 3.97 mm,respectively,and theηis increased by 0.074;its mixing efficiency is decreased by 4.46%,but the thrust is increased by 2.29%and the total pressure loss is decreased by 0.64%.Downstream of the optimized lobed mixer,the radial scale and peak vorticity of the streamwise voritices decrease with the decreasing lobe height,thereby reducing the mixing efficiency.For the optimized lobed mixer,its low mixing efficiency is the main factor for the decrease of the total pressure loss,but the improvement of the geometric curvature is also conducive to reducing its profile loss.Within the scope of this study,the lobed mixer has an optimal mixing efficiency(ε=74.14%)that maximizes its thrust without excessively increasing the mixing loss.
基金Supported by the National Natural Science Foundation of China(21276078)"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500)the Shanghai Science and Technology Program(13QH1401200)
文摘Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
