Configuring the Evolutionary Optimization (EVOL) Algorithm

Double-click the Optimization icon .
The Optimization Component Editor appears.
From the General tab's Optimization Technique list, select Evol.

In the Optimization Technique Options area, enter or select the following:

Option	Description
Maximum Evaluations	This parameter sets the maximum number of evaluations. The default is 100.
Convergence Tolerance	This parameter controls the termination criteria of the algorithm. When the absolute value of the difference in the ObjectiveAndPenalty parameter between two consecutive design points is below this parameter, the algorithm stops. The default is 0.01. Other possible values are $0 < \times \leq 1.0$ .
Minimum Discrete Step	The Evolutionary Optimization Algorithm varies input parameters such that the relative change in parameter value, expressed as a percentage of the total range of the variable, is always a multiple of the Minimum Discrete Step. The default value is 0.02 (2% of the total variable range). Other possible values are $> 0$ and $< 1.0$ (0 to 100% of the total variable range).
Consecutive Variable Search	This option forces the Evolutionary Optimization Algorithm to vary only one variable at a time when performing the search. The default value is false (all variables are varied at the same time). The other possible value is true (only one variable at a time is varied).
Parallel Batch Size	This parameter specifies the batch size that the Evolutionary Optimization Algorithm submits for parallel execution. The actual number of parallel simulation process flow executions may be further limited by the number of available stations. If the Enable parallel execution option is not selected, the Parallel Batch Size value has no effect. The default value is 1.0 (no parallel execution). Other possible values are $> 1$ .
Penalty Base	The Evolutionary Optimization Algorithm evaluates the quality of a design point using the combined value of the objective function and penalty function. When calculating the penalty function of the design, the Penalty Base option can be used for all designs that violate at least one constraint. This allows the technique to better differentiate feasible designs with a slightly higher objective function from infeasible designs with a slightly lower objective function. The total penalty function is calculated as follows: $P e n a l t y = P e n a l t y B a s e + P e n a l t y M u l t i p l i e r \times S u m ({V i o l a t i o n}_{i} \times W_{i} / S_{i}) \land P e n a l t y E x p o n e n t$ where ${V i o l a t i o n}_{i}$ is the $i - t h$ constraint violation value, $W_{i}$ is the corresponding weight factor, and $S_{i}$ is the corresponding scale factor. The penalty base is set to zero if no constraints are violated. The default value is 0.0.
Penalty Multiplier	This parameter is used to increase or decrease the effect of the total constraint violations on the measure of the design quality. The default value is 1000.0.
Penalty Exponent	This parameter can be used to increase or decrease the nonlinearity of the effect of the total constraint violations on the penalty function value. The value type is integer. The default value is 2.
Maximum Failed Runs	This parameter is used to set the maximum number of failed subflow evaluations that can be tolerated by the optimization technique. If the number of failed runs exceeds this value, the optimization component will terminate execution. To disable this feature, set this option to any negative value (e.g., –1). When this option is set to a negative value, the otimization will continue execution despite any number of failed subflow runs.
Failed Run Penalty Value	This parameter represents the value of the Penalty parameter that is used for all failed subflow runs. The default value is $1 \times 10^{30}$ .
Failed Run Objective Value	This parameter represents the value of the Objective parameter that is used for all failed subflow runs. The default value is $1 \times 10^{30}$ .
Use fixed random seed	If this option is selected, the random number generator used by the optimization algorithm is seeded using the value specified in the corresponding text box. All executions of the Optimization component will use exactly the same sequence of random numbers and, therefore, will produce exactly the same design points. This arrangement is useful for debugging the optimization process when it is necessary to reproduce the same sequence of design points. If this option is not selected, the random number generator is seeded by using the clock time at the moment of execution.

Click OK to save your changes and to close the Optimization Component Editor.