About Optimization Techniques

Several of the optimization techniques are designed to directly optimize multiple objectives at one time. These multi-objective techniques are AMGA, Multi-Objective Particle Swarm, NCGA, and NSGA-II. All other optimization techniques are not capable of handling several objectives directly and require that a single objective function be formed from all selected objective parameters.

The value of the objective function is calculated as a sum of all objective components with corresponding weight and scale factors:

(Objective⁢=Sum⁢(OBJi× Wi⁢/Si),

where OBJi is the contribution to the objective function of the i−th objective component (parameter), Wi is the corresponding weight factor, and Si is the corresponding scale factor.

If the direction of a specific objective parameter is “minimize,” its contribution to the objective function equals the parameter value itself:

OBJi= Parmi

If the direction of a specific objective parameter is “maximize,” its contribution to the objective function equals the parameter value multiplied by –1 to reverse the direction, because the Optimization component always minimizes the objective function:

OBJi= Parmi=−1.0⁢× Parmi

If the direction of a specific objective parameter is “target,” its contribution to the objective function is calculated as follows:

OBJi= Sqrt[(Parmi−Targeti)2+0.04]−0.2