The Opposed Multi-Burner (OMB) Coal-Water Slurry (CWS) gasification is a new large-scale coal gasification technology with higher product yield, lower oxygen and coal consumption than that of Texaco CWS gasification t...
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In this paper,we consider fast and desired consensus of directed network via pinning ***,we provide a sufficient condition for the stability of desired consensus ***,we investigate the problem of selecting optimal pin...
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ISBN:
(纸本)9781479900305
In this paper,we consider fast and desired consensus of directed network via pinning ***,we provide a sufficient condition for the stability of desired consensus ***,we investigate the problem of selecting optimal pinned nodes for driving fastest consensus,which is formulated as an Mixed-Integer Semidefinite ***,we illustrate all the results by simulating on some typical directed networks.
Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compres...
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Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.
Competitive swarm optimizer(CSO) has shown promising results for solving large scale global optimization problems proposed ***,CSO shows insufficient exploitation of the *** this paper,a competitive swarm optimizer ...
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ISBN:
(纸本)9781538629185
Competitive swarm optimizer(CSO) has shown promising results for solving large scale global optimization problems proposed ***,CSO shows insufficient exploitation of the *** this paper,a competitive swarm optimizer integrated with Cauchy and Gaussian mutation(CGCSO) is proposed for large scale *** new algorithm does not only update the losers’ positions with the CSO method,but also update the winners’ positions by Cauchy and Gaussian mutation to improve the exploitation capability of the ***,CGCSO utilizes the ring topology to enhance the swarm diversity and alleviate premature convergence.A comparative study between CGCSO and CSO evaluated on the CEC’08 benchmark functions has been carried *** experimental results indicate that CGCSO performs better on the whole,especially on the non-separable functions and 500-dimensional problems.
In this study, the lth order (l≥2) consensus problem for multi-agent systems is considered, which generalises the existing second-order consensus algorithm. A linear consensus protocol is proposed for solving such a ...
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An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti...
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An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.
As one of the crucial semiconductor manufacturing steps, chip packaging process plays an important role to provide a phys ical protection, electric interconnection environment for naked wafer. In the present work, the...
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Brain computer interface (BCI) could help patients to manipulate external devices based on the specific brain activities. One of the most popular BCI systems is the visual-based BCI system. Mostly, users were asked to...
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Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper, a modified Bare-bones MOPSO algorithm is proposed that takes advantage of few parameters of...
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Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper, a modified Bare-bones MOPSO algorithm is proposed that takes advantage of few parameters of bare-bones algorithm. To avoid premature convergence, Gaussian mutation is introduced;and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution. Finally, by combining the algorithm with control vector parameterization, an approach is proposed to solve the dynamic optimization problems of chemicalprocesses. It is proved that the new algorithm performs better compared with other classic multi-objective optimization algorithms through the results of solving three dynamic optimization problems.
Dear editor,With the development of high-performance computing techniques, run time of meta-heuristic algorithms can be reduced effectively. However, improved computation power has not been indirectly converted into s...
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Dear editor,With the development of high-performance computing techniques, run time of meta-heuristic algorithms can be reduced effectively. However, improved computation power has not been indirectly converted into search capability in most of previous studies [1]. Moreover, the solution precision of an optimization problem is directly determined
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