In the paper, multi-objective genetic algorithm was used to solve TEP problem in an IEEE standard 24-bus experimental system Fig. \ref{879677}. The basis data of the network were provided from reference \cite{Subcommittee_1979} and the data related to initial investment cost were taken from reference \cite{Zhao_2011}. It is assumed that the system has to be developed for future condition in which load and generation demand is 2.2 times higher than the initial level (initial load was 3054, initial generation was 3404 MW, so 2.2 times higher equals a load of 6720 MW and generation level of 7490 MW. This is equal to increase rate of 8% per year in a five-year planning’s horizon.
In addition, it is assumed that the candidate branches of network development can be done simultaneously in all current 34 lines, and 7 new lines to be added in future, the their data of which are presented in Table \ref{tab:1}. It is noteworthy that the information of candidate lines which are in parallel with previous lines, be like them exactly. Due to environmental limitations, 3 lines can be installed in each route. It is also assumed that all generators can be upgraded to 1.3 times higher than their current capacity and if more capacity is needed, new plants have to be constructed.