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The characteristics of a device (size, shape, material..) that significantly influence the performance, that can be parameterized and that are subject to constraints are designated as design variables.

The constraints create a design space of devices which satisfy the specifications.

The aspects of performance that need to be either maximized or minimized is mathematically defined as an objective function. Multiple objectives are each assigned a value and used in a weighted sum to determine their global priority.

An Evolutionary-based Stochastic search approach is used to explore the design space to obtain an improved configuration of the device (based on the objective function). The initial values entered are treated as imprecise, alleviating the burden of determining appropriate initial conditions for proper optimization.

The process is as follows:

• A point is chosen in the design space
• The objective function is evaluated at that point
• The parameters are varied along the hyper space representing the objective function to check for improvements
• Iterate until no further improvement in the objective function is achieved

Optimization Controls

Maximum number of solutions the optimizer will try

Specify the search tolerance, which represents the minimum step length (per unit) that the optimizer can move within the feasible region

Time limit feature which stops the optimizer if it exceeds the time indicated