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Both problems represent combinatorial optimizations based on multi-objective functional costs and were solved efficiently by genetic algorithms.
These objective functions are combined in a multi-objective function.
To speedup the calculation, we parallelized the objective functional evaluation step using multi-threading with the Open Multi-Processing (OpenMP) library (Pacheco, 2011).
We consider a multi-objective in terms of the compliances under multi boundary conditions, and use the weighted sum compliance as the objective functional and minimize it under the volume and the state equation constraints.
The objective functional is given by (1.2).
An iteration scheme is developed to solve the objective functional.
data-centric multi objective QoS-aware routing protocol.
Incorporating multi-objective optimization of functional and non-functional properties, however, is an important and necessary step for our future work.
Regarding the optimization of the objective function, the functional, multi-dimensional, static, constraint, continuous, single-objective, and meta-heuristic optimizations were applied.
Some objective functionals are formulated.
The crisp model described is in the form of crisp-Multi-Objective Linear Programming (MOLP) with objective functions, functional constraints and non-negativity constraints.
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