In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical...In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.展开更多
Heat exchangers are widely used in the process engineering such as the chemical industries, the petroleum industries, and the HVAC applications etc. An optimally designed heat exchanger cannot only help the optimizati...Heat exchangers are widely used in the process engineering such as the chemical industries, the petroleum industries, and the HVAC applications etc. An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption. In this paper, a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger. This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts. In AOC, the hexagram operations in I Ching are generalized to binary string case and an iterative process, which imitates the I Ching inference, is defined. Before applying the AOC to the heat exchanger design problem, the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP). Based on the TSP results, the AOC is shown to be superior to the genetic algorithms (GA). The AOC is then used in the optimal design of heat exchanger. The shell inside diameter, tube outside diameter, and baffles spacing are treated as the design (or optimized) variables. The cost of the heat exchanger is arranged as the objective function. For the heat exchanger design problem, the results show that the AOC is comparable to the GA method. Both methods can find the optimal solution in a short period of time.展开更多
According to traditional card problem solving which is based on the idea of genetic algorithm(GA),a set of algorithms is designed to find final solution.For each process in genetic algorithm,including choices of fitne...According to traditional card problem solving which is based on the idea of genetic algorithm(GA),a set of algorithms is designed to find final solution.For each process in genetic algorithm,including choices of fitness function,parameters determination and coding scheme selection,classic algorithm is used to realize the various steps,and ultimately to find solution of problems.展开更多
文摘In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.
基金supported by Science and Technology Development Fund of Macao SAR (Grant No. 033/2008/A2)Research Grant of University of Macao, China (Grant No. RG081/09-10S/TSC/FST)
文摘Heat exchangers are widely used in the process engineering such as the chemical industries, the petroleum industries, and the HVAC applications etc. An optimally designed heat exchanger cannot only help the optimization of the equipment size but also the reduction of the power consumption. In this paper, a new optimization approach called algorithms of changes (AOC) is proposed for design and optimization of the shell-tube heat exchanger. This new optimization technique is developed based on the concept of the book of changes (I Ching) which is one of the oldest Chinese classic texts. In AOC, the hexagram operations in I Ching are generalized to binary string case and an iterative process, which imitates the I Ching inference, is defined. Before applying the AOC to the heat exchanger design problem, the new optimization method is examined by the benchmark optimization problems such as the global optimization test functions and the travelling salesman problem (TSP). Based on the TSP results, the AOC is shown to be superior to the genetic algorithms (GA). The AOC is then used in the optimal design of heat exchanger. The shell inside diameter, tube outside diameter, and baffles spacing are treated as the design (or optimized) variables. The cost of the heat exchanger is arranged as the objective function. For the heat exchanger design problem, the results show that the AOC is comparable to the GA method. Both methods can find the optimal solution in a short period of time.
文摘According to traditional card problem solving which is based on the idea of genetic algorithm(GA),a set of algorithms is designed to find final solution.For each process in genetic algorithm,including choices of fitness function,parameters determination and coding scheme selection,classic algorithm is used to realize the various steps,and ultimately to find solution of problems.
