In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was proposed.Firstly,RMMA introduced the mechanism of random mating into the mayfl...In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was proposed.Firstly,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)problem.Secondly,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the boundary.Lastly,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and MA.The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP problems.The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms.When the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms.RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.展开更多
This paper deals with the problem of project scheduling subject to multiple execution modes with non-renewable resources, and a model that handles some of monetary issues in real world applications.The objective is to...This paper deals with the problem of project scheduling subject to multiple execution modes with non-renewable resources, and a model that handles some of monetary issues in real world applications.The objective is to schedule the activities to maximize the expected net present value(NPV) of the project, taking into account the activity costs, the activity durations, and the cash flows generated by successfully completing an activity.Owing to the combinatorial nature of this problem, the current study develops a hybrid of branch-and-bound procedure and memetic algorithm to enhance both mode assignment and activity scheduling.Modifications for the makespan minimization problem have been made through a set of benchmark problem instances.Algorithmic performance is rated on the maximization of the project NPV and computational results show that the two-phase hybrid metaheuristic performs competitively for all instances of different problem sizes.展开更多
A lagoon in the New Binhai District, a high-speed developing area, Tianjin, China, has long been receiving the mixed chemical industrial wastewater from a chemical industrial park. This lagoon contained complex hazard...A lagoon in the New Binhai District, a high-speed developing area, Tianjin, China, has long been receiving the mixed chemical industrial wastewater from a chemical industrial park. This lagoon contained complex hazardous substances such as heavy metals and accumulative pollutants which stayed over time with a poor biodegradability. According to the characteristics of wastewater in the lagoon, the micro-electrolysis process was applied to improve the biodegradability before the bioprocess treatment. By the orthogonal experimental study of main factors influencing the efficiency of the treatment method, the best control parameters were obtained, including pH=2.0, a volume ratio of Fe and reaction wastewater of 0.03750, a volume ratio of Fe and the granular activated carbon (GAC) of 2.0, a mixing speed of 200 r/min, and a hydraulic retention time (HRT) of 1.5 h. In the meantime, the removal rate of chemical oxygen demand (COD) was up to 64.6%, and NH4+-N and Pb in the influent were partly removed. After the micro-electrolysis process, the ratio of biochemical oxygen demand (BOD) to COD (B/C ratio) was greater than 0.6, thus providing a favorable basis for bioprocess treatment.展开更多
A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The f...A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
基金supported by the National Natural Science Foundation of China(61761004)。
文摘In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was proposed.Firstly,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)problem.Secondly,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the boundary.Lastly,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and MA.The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP problems.The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms.When the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms.RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.
文摘This paper deals with the problem of project scheduling subject to multiple execution modes with non-renewable resources, and a model that handles some of monetary issues in real world applications.The objective is to schedule the activities to maximize the expected net present value(NPV) of the project, taking into account the activity costs, the activity durations, and the cash flows generated by successfully completing an activity.Owing to the combinatorial nature of this problem, the current study develops a hybrid of branch-and-bound procedure and memetic algorithm to enhance both mode assignment and activity scheduling.Modifications for the makespan minimization problem have been made through a set of benchmark problem instances.Algorithmic performance is rated on the maximization of the project NPV and computational results show that the two-phase hybrid metaheuristic performs competitively for all instances of different problem sizes.
基金Project supported by the National Natural Science Foundation of China (No. 70833003)the National Science and Technology Support Project of 11th 5-Year Plan, China (No. 200603746006)
文摘A lagoon in the New Binhai District, a high-speed developing area, Tianjin, China, has long been receiving the mixed chemical industrial wastewater from a chemical industrial park. This lagoon contained complex hazardous substances such as heavy metals and accumulative pollutants which stayed over time with a poor biodegradability. According to the characteristics of wastewater in the lagoon, the micro-electrolysis process was applied to improve the biodegradability before the bioprocess treatment. By the orthogonal experimental study of main factors influencing the efficiency of the treatment method, the best control parameters were obtained, including pH=2.0, a volume ratio of Fe and reaction wastewater of 0.03750, a volume ratio of Fe and the granular activated carbon (GAC) of 2.0, a mixing speed of 200 r/min, and a hydraulic retention time (HRT) of 1.5 h. In the meantime, the removal rate of chemical oxygen demand (COD) was up to 64.6%, and NH4+-N and Pb in the influent were partly removed. After the micro-electrolysis process, the ratio of biochemical oxygen demand (BOD) to COD (B/C ratio) was greater than 0.6, thus providing a favorable basis for bioprocess treatment.
基金supported by the National Natural Science Foundation of China(71171038)
文摘A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
