Abstract:
Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different
combinatorial optimization problems. In this method, a number of artificial ants build solutions
for an optimization problem and exchange information on their quality through a communication
scheme that is similar to the one adopted by real ants. In this thesis, Ant Colony Optimization is
used to solve reconfiguration of a benchmark distribution system consisting of 33 buses for loss
minimization. Solving this problem is a formidable task even for a simple distribution network as
the number of possible switching options that are to be considered is numerous. The results
obtained using any meta-heuristic method strongly depends on the control parameter values
chosen. The proposed algorithm is coded in MATLAB, and power world simulator to check the
result obtained by MATLAB. The performance of the proposed technique is tested on two
standard distribution systems with size (33 buses). The optimal configuration result shows that
31.9% and reduction of loss from the initial configuration in the test case. And also significant
reductions in CPU time achieved.
