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The primary principle in these evolutionary clustering frameworks is guided by decomposing the problem into two individual criteria, snapshot quality and temporal smoothness.
To deal with this issue, this paper formulates the VN embedding problem as a new multiple objective linear programming optimization program, and solves it in a preemptive strategy by decomposing the problem into node mapping and link mapping phases.
QA-Learning reduces the complexity of large state space problems by decomposing the problem into more manageable sub-problems, and distributing agents between these sub-problems, to improve efficiency and enhance parallelisation [2].
In this regard, we decide to propose a distributed optimal scheduling model by decomposing the problem (10) into M local subproblems, which can be implemented by the MGs in an autonoums and cooperative manner.
Two different approaches have been considered in this paper: (1) Solving the problem sequentially by decomposing the problem into two sub-problems- the berth allocation and the dynamic allocation of ship un-loaders in different berths (2) solving the problem by integrating berth allocation and dynamic allocation problem.
Compared with the canonical genetic algorithm, the new algorithm is innovative in four respects: (1) The new algorithm addresses epistasis of genes by decomposing the problem variables into evolutionary variables, which evolve with the genetic operators, and the optimization variables, which are derived by solving corresponding optimization problems.
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And a heuristic algorithm for the ICFLD problem is developed by decomposing the ICFLD problem into two subproblems and iteratively and alternately solving the decomposed subproblems.
A case study of the effect of heterogeneity in matrix diffusion for a single fracture could be performed by decomposing the transport problem into a one-dimensional mass transfer problem and a two-dimensional flow problem using a Lagrangian method of description.
ECOC is a widely used and successful technique, which implements a multi-class classification system by decomposing the original problem into several two-class problems.
Stability of the overall scheme is established based on the small gain theorem, and by decomposing the synthesis problem for the distributed information flow filter the problem size is reduced to that of a single agent.
This is achieved by decomposing the master problem and solving only the much smaller sub-problems resulting from decomposition.
More suggestions(15)
by avoiding the problem
by summarizing the problem
by decomposing the system
by decomposing the concentration
by decomposing the master
by decomposing the term
by decomposing the response
by decomposing the prediction
by decomposing the information
by decomposing the delay
by decomposing the rating
by decomposing the flow
by decomposing the covariance
by decomposing the rate
by decomposing the domain
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