An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging per...An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times.展开更多
With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex ele...With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search...A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.展开更多
To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） alg...To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.展开更多
This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching ph...This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.展开更多
Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence reg...Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.展开更多
A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociol...A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.展开更多
In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment a...In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.展开更多
To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damagin...To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.展开更多
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustab...A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.展开更多
生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论...生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论文为依据,借助文献计量学软件CiteSpace.6.3R1,介绍2013—2023年间国内外研究热点的发展现状和情况;根据每种智能算法在生态学优化、预测和评估研究中的作用,分类论述其实际研究过程和应用特征;分析智能算法应用的优势和当前存在的局限性;回顾智能算法对生态学研究的意义,并提出了对未来发展前景的展望。展开更多
文摘An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times.
文摘With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
基金supported by the National Natural Science Foundation of China(60870004)
文摘A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
基金supported by the National Natural Science Foundation of China(60771063).
文摘To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.
基金supported by the National Natural Science Foundation of China(61601505)
文摘This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.
基金Supported by National Natural Science Foundation of China(61074020)
文摘Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
基金Projects(51105157, 50875101) supported by the National Natural Science Foundation of ChinaProject(2009AA043301) supported by the National High Technology Research and Development Program of China
文摘A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.
基金Project (70671039) supported by the National Natural Science Foundation of China
文摘In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.
基金Sponsored by Beijing Priority Laboratory Fund of China(SYS10070522)
文摘To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.
文摘A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
文摘生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论文为依据,借助文献计量学软件CiteSpace.6.3R1,介绍2013—2023年间国内外研究热点的发展现状和情况;根据每种智能算法在生态学优化、预测和评估研究中的作用,分类论述其实际研究过程和应用特征;分析智能算法应用的优势和当前存在的局限性;回顾智能算法对生态学研究的意义,并提出了对未来发展前景的展望。
