constraint function


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constraint function

[kən′strānt ‚fəŋk·shən]
(mathematics)
A function defining one of the prescribed conditions in a nonlinear programming problem.
References in periodicals archive ?
In this paper, we propose an approximate algorithm based on membrane computing for constrained optimization problems: a membrane associates a constraint and the tentative solutions evolve according to the rules in the membrane and are evaluated by the constraint function value as the fitness; the sub-populations can communicate efficiently during the evolution process by making use of the structure of P systems and the communication mechanism among the membranes.
Rewriting the fuzzy linear programming (13) with the above inequalities, the extended operation of the product of the fuzzy numbers and variable x of the constraint function is given as:
In our proposed model, the constraint-handling method needs to calculate, for each cell (solution) regardless of the population to which it belongs, the following: 1) value of each constraint function, 2) sum of violation constraints (sum-res), it is a positive value determined by the addition of [[g.
The constraint function is the producer's cost constraint which is conventionally given as
Additionally, for the product mix to be feasible, the setup times and process times must be included in the same constraint function due to the resource-consuming nature of the setup activity.
The objective has to be reached under the condition that pressure consumption is same or less than the initial distributor; this condition translates into a constraint function (g).
Also, we may want to protect environment agents from being killed, so we may have to ensure that the constraint function C will always return true when applied to an environmental agent.
2005) often leads to high numbers of expensive function evaluations especially in the case when cost and constraint function resulted from finite element simulations fine meshes, nonlinear geometrical and material behavior etc.
2] are positive and only one constraint function among them is positive at [[Alpha].
The approximation of the objective and constraint functions is modelled by use of a neural network and search for an optimal design is accomplished by applying genetic algorithm.

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