The multiple objective optimization problems are solved by an evolutionary algorithm using a Matlab program.
Such joint optimization of multiple competing optimization problems is very difficult, if not impossible, to be modelled and solved with traditional multi-objective optimization methods.
In the algorithm, the global network parameter optimization problem is divided into multiple simple optimization problems, one per adjacency, where a regular scenario is assumed.
To design robust fault detection filters, two fault detection design problems are formulated as multiple objective optimization problems by minimizing the effects of disturbances and maximizing the fault sensitivity involving an LTI system with disturbance and fault signals.
In this study, a four-step approach was presented to find the optimal treatment in multiple-response optimization problems.
For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set.
As applications of our results, we study multiple sets split monotone inclusion problems for inverse strongly monotone mappings, multiple sets split optimization problems, and multiple sets split feasibility problems.
GAs are particularly useful to one-objective and multiple-objective optimization problems [6] that make one- or multi-objective attainment to the optimum.
We combine swarm particle algorithms with Nash strategy based on noncooperative game theory to obtain the Nash equilibrium in multiple-objective optimization problems.
The set Ξ of non-dominated (or Pareto-optimal) designs for the multiple objective optimization problem defined by log| M β |, and log| M ν | is defined by The solid line in Figure 8 is an example of a Pareto surface for simultaneous maximization of the two criteria.
