Sentence examples for multidimensional optimization problem from inspiring English sources
In particular, the proposed SIA directly optimizes the objective function and its each iteration turns the multidimensional optimization problem into multiple one-dimensional optimization problems with closed-form solutions.
The determination of the most relevant seeming scenario for us is, in fact, a multidimensional optimization problem that cannot be solved by the calculation of the overall probability.
The representation of multiexponential kinetics of the basic reaction in the form of a sum of exponential functions ( left(A t)={displaystyle {sum}_{i=1}^n{a}_i{e}^{-{k}_it}}right) ) is a multidimensional optimization problem.
A multidimensional optimization problem is given, along with an objective function to evaluate the fitness of each candidate point in parameter space; the swarm is typically modeled by particles in this multidimensional space that have a position and a velocity.
The precedence-constrained production planning and scheduling is a multidimensional optimization problem, in which a number of sub-problems such as production selection, product allocation, manufacturing sequence, etc. are required to be simultaneously solved.
The representation of multiexponential kinetics curve for the basic reaction in the form of a sum of exponential terms ( left(A t)={{displaystyle {sum}_{i=1}^n{a}_ie}}^{-{k}_it}right) ) with restriction (k i > 0, a i > 0), is a multidimensional optimization problem with identification difficulties.
Several concerns in sensor networks are formulated as multidimensional optimization problems, and approximated through computational intelligence techniques.
In comparison with other anomaly detection algorithms, ABC has a number of advantages which can be numerated as (1) detection of discord patterns in a large non linear data during a short time, (2) simplicity, (3) having less control parameters and (4) efficiently for solving multimodal and multidimensional optimization problems.
Unfortunately, they often lead to difficult multidimensional optimization problems with a heavy computational burden.
A surface with multiple local maxima and minima can simultaneously test a GA's ability to avoid premature convergence and its ability to handle multidimensional optimization problems.
The main challenges in this topic involve the ill-posedness of the estimation problem such that many indistinguishable solutions exist [ 4], as well as the high computational cost in solving the associated multidimensional global optimization problem.
