A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point....A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.展开更多
Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst cas...Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.展开更多
Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware.Furthermore,a minimum explanation of infeasibility that ex...Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware.Furthermore,a minimum explanation of infeasibility that excludes all irrelevant information is generally of interest.A smallest-cardinality unsatisfiable subset called a minimum unsatisfiable core can provide a succinct explanation of infea-sibility and is valuable for applications.However,little attention has been concentrated on extraction of minimum unsatisfiable core.In this paper,the relationship between maximal satisfiability and mini-mum unsatisfiability is presented and proved,then an efficient ant colony algorithm is proposed to derive an exact or nearly exact minimum unsatisfiable core based on the relationship.Finally,ex-perimental results on practical benchmarks compared with the best known approach are reported,and the results show that the ant colony algorithm strongly outperforms the best previous algorithm.展开更多
Separator design in petroleum engineering is so important because of its important role in the evaluation of optimum parameters and also to achieve to maximum stock tank liquid. However, no simulator exists that simul...Separator design in petroleum engineering is so important because of its important role in the evaluation of optimum parameters and also to achieve to maximum stock tank liquid. However, no simulator exists that simultaneously and directly optimizes the parameters “pressure”, “temperature”, and so on. On the other hands, Commercial simulators fix one parameter and vary another parameter to achieve the optimum conditions. So, they need long-time simulation. Moreover, gas condensate reservoirs, like another reservoirs, have this problem as well. In present paper, a self-developed simulator applied in the optimized design of gas condensate reservoir’s separators by determining optimized pressure, temperature, and number of separators in order to obtain maximized tank liquid volume and minimized tank liquid density utilizing Matlab software and other commercial simulators such as Aspen-Plus, Aspen-Hysys, and PVTi to do a comparison. Also, each software was separately tested with one, two, and three separators to obtain the optimum number of separators. Additionally, Peng-Robinson equation of state (PR EOS) has been applied in the simulation. For simulation input, a set of field data of gas condensate reservoir has been utilized, as well. The results show a good compatibility of this simulator with other simulators but in so little runtime (this simulator calculates the optimum pressure and temperature in a wide range of pressures and temperatures with the help of a simultaneous optimization algorithm in one stage) and the highest stock tank liquid is calculated with this simulator in comparison to other simulators. Also, with the help of this simulator, we are able to obtain the optimum pressure, temperature, and the number of separators in the gas condensate reservoir’s separators with any desired properties. Finally, this simulator optimizes the temperatures for each separator and obtains very good results despite the other simulators that fix temperatures for all separators in most times.展开更多
文摘A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.
文摘Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.
基金the National Natural Science Foundation of China (No.60603088)
文摘Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware.Furthermore,a minimum explanation of infeasibility that excludes all irrelevant information is generally of interest.A smallest-cardinality unsatisfiable subset called a minimum unsatisfiable core can provide a succinct explanation of infea-sibility and is valuable for applications.However,little attention has been concentrated on extraction of minimum unsatisfiable core.In this paper,the relationship between maximal satisfiability and mini-mum unsatisfiability is presented and proved,then an efficient ant colony algorithm is proposed to derive an exact or nearly exact minimum unsatisfiable core based on the relationship.Finally,ex-perimental results on practical benchmarks compared with the best known approach are reported,and the results show that the ant colony algorithm strongly outperforms the best previous algorithm.
文摘Separator design in petroleum engineering is so important because of its important role in the evaluation of optimum parameters and also to achieve to maximum stock tank liquid. However, no simulator exists that simultaneously and directly optimizes the parameters “pressure”, “temperature”, and so on. On the other hands, Commercial simulators fix one parameter and vary another parameter to achieve the optimum conditions. So, they need long-time simulation. Moreover, gas condensate reservoirs, like another reservoirs, have this problem as well. In present paper, a self-developed simulator applied in the optimized design of gas condensate reservoir’s separators by determining optimized pressure, temperature, and number of separators in order to obtain maximized tank liquid volume and minimized tank liquid density utilizing Matlab software and other commercial simulators such as Aspen-Plus, Aspen-Hysys, and PVTi to do a comparison. Also, each software was separately tested with one, two, and three separators to obtain the optimum number of separators. Additionally, Peng-Robinson equation of state (PR EOS) has been applied in the simulation. For simulation input, a set of field data of gas condensate reservoir has been utilized, as well. The results show a good compatibility of this simulator with other simulators but in so little runtime (this simulator calculates the optimum pressure and temperature in a wide range of pressures and temperatures with the help of a simultaneous optimization algorithm in one stage) and the highest stock tank liquid is calculated with this simulator in comparison to other simulators. Also, with the help of this simulator, we are able to obtain the optimum pressure, temperature, and the number of separators in the gas condensate reservoir’s separators with any desired properties. Finally, this simulator optimizes the temperatures for each separator and obtains very good results despite the other simulators that fix temperatures for all separators in most times.
