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Coupled optimization methods based on multi-objective genetic algorithms and approximation models are widely used in engineering optimizations.
Mechanism reduction methods can produce reduced schemes that provide almost identical simulation results using much less computer time, thus allowing engineering optimizations.
Considering the different requirements for decision and state variables in engineering optimizations, an improved multi-objective particle swarm optimization with preference strategy (IMPSO-PS) is presented and applied to the optimal integration of distributed generation (DG) into the distribution system.
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In this paper experiments for a further biochemical engineering optimization of the disintegration step are presented.
Therefore, the configuration of groundwater monitoring networks and the number of required wells becomes an important engineering optimization problem.
Curse of dimensionality is a key issue in engineering optimization.
Many engineering optimization problems involve decision variables of continuous nature.
Simulations were performed over miscellaneous engineering optimization problems that include minimization of constrained functions and structural engineering optimization.
Various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of the HS algorithm.
Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms.
The proposed algorithm offers higher accuracy in engineering optimization problems for constrained optimization problems.
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