One of the optimization problems is that when input variables are discrete, it is more difficult to find the optimum solution compared to continuous values. Equipped with various optimization algorithms, Optimus, an optimized design support tool, helps you efficiently search for the optimum solution even when input variables are discrete. Here are two cases where optimization problems related to discrete values are dealt with using Differential Evolution, an evolutionary algorithm that has recently been drawing attention.
To position a magnetic head on a fast-running hard disk drive, it is necessary to suppress oscillation of the head tip. In this optimization case, the node of oscillation is utilized to control the mode shape so that the magnetic head tip hardly moves from the original position.
Optimization reduces head tip displacement by 90% compared to the basic model.
Switching power supplies are widely used as power for electronic devices, but noise suppression is becoming essential given faster speeds. In this case, the most appropriate combination of switching element, noise filter and snubber circuit is defined as a measure to reduce noise.
Noise is reduced by 30% compared to the worst case in the optimization process.
Differential Evolution is one of the evolutionary algorithms proposed by R. Storn and K. Price in 1995. As an optimization method to obtain the global optimum, it is receiving significant attention along with Particle Swarm Optimization and Ant Colony Optimization.
The optimum solution can be obtained even when input values contain discrete values.
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