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The objective function is optimized using non-linear, multi-resolution optimization techniques.
Usually, the cost function is optimized subject to statistical properties.
In second part, by considering number of decision variables, the objective function is optimized.
In the logic synthesis step, the logic function is optimized using a logic synthesis tool without considering the clock network.
Energy consumption of the process as an objective function is optimized by describing key variables of the design.
In order to tune the PI gains, the control objective function is optimized using the genetic algorithm (GA).
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Parameter values for the classification function were optimized using particle swarm optimization where the parameter spaces for the three parameters were explored (ranges in brackets): M [-20, 30], K [0, 2], and D [30, 210].
The weighting coefficients of the cost function were optimized interactively by simulation.
The major challenge of solving this problem is that the objective function being optimized is characterized by a multitude of local minima separated by high-energy barriers.
In order to optimize three separation factors and study the interaction between factors, a response function was optimized via searching its optimum (minimum/maximum).
The hydration free energy function was optimized and validated using the two data sets.
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