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The complicated task of design optimization of compact heat exchangers (CHEs) have been effectively performed by using evolutionary algorithms (EAs) in the recent years.
This work presents an adaptive cascade controller tuned by using evolutionary algorithms for the trajectory tracking control of a hydraulic actuator with an overlapped proportional valve.
In this work, a damage identification methodology is presented which allows combining static and dynamic measurements within a model updating procedure posed in a multi-objective framework solved by using evolutionary algorithms.
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The optimal architecture of neural network can also be optimized by using evolutionary algorithm in finding nonfunctional SNPs.
Systematic system design methodology followed by multi-objective optimization technique using evolutionary algorithms are carried out to provide a set of candidate solutions considering different configurations and conflictive objectives such as efficiency, cost and environmental impact.
This study demonstrates the performance improvement of an air-to-air 7.5 Ton (26.4 kW) rooftop unit (RTU) achieved by optimizing an evaporator's refrigerant circuitry using evolutionary algorithms.
Robot controllers were evolved in a simulated environment using evolutionary training algorithms.
Finally, the optimal values of decision variables are obtained by minimizing the objective function using evolutionary genetic algorithm.
The numerical study was conducted by using an evolutionary algorithm coupled with the software EnergyPlus® used in simulations.
The total cost rate of the system is minimized while the cycle exergy efficiency is maximized by using an evolutionary algorithm.
This methodology can be easily adapted to solve the DSESP, in this case by using an evolutionary algorithm as global approach.
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