*5.3.7 Hybridization of particle swarm optimization algorithm (PSOA) based ant-lion optimization algorithm (ALOA)*

In this part of hybridization is, the PSOA has been associated to enhance the ALOA performance, by updating the ALOA elitism stage.

Initially the load flow analysis is carried out with the help of Backward/Forward (BW/FW) sweep method for the base case with the bus data and the load data of the test system without adding any extra load on to the system. Now with intend to reduce these power losses up to maximum possible extent the PSOA based ALOA is combined to form a hybrid methodology. With this methodology, the candidate busses, and capacities of DGs are estimated with the help of ALOA method, and then the locations of antlions are updated with the help of PSOA methodology. This will be evaluated with the help of the flowchart given for proposed hybrid methodology.

#### *5.3.8 Specific contribution*

Initially by using the ALOA standalone algorithm the DG locations and capacities were identified intend to reduce the total power losses and total operating cost and enhancement of voltage stability index and the voltage profile at every bus. All the values have been tabulated with different power factor conditions on various test networks with different types of DG units.

Now with the help of proposed hybrid algorithm the DG locations and capacities were identified intend to reduce the total power losses and total operating cost and enhancement of voltage stability index and the voltage profile at every bus. All the values have been tabulated with different power factor conditions on various test networks with different types of DG units.

*Particle Swarm Optimization DOI: http://dx.doi.org/10.5772/intechopen.107156*

#### **Figure 7.**

*Flow chart of proposed PSOA-ALOA hybrid technique [6].*

In ALOA hybridized with PSOA the elitism phase of the ALOA for DG position were updated by using the following mathematical equations,

$$\begin{aligned} \text{new\\_velocity}\_{j} \ (kk+1) &= \cos \tan t \ ^\* \ \text{old\\_velocity}\_{j} \ (kk) \\ \text{\\_} &+ \text{inertia\\_cons\\_tax}\_{1\*} \ \text{positive\\_cons\\_tax} \ \text{t}\_{1} \ ^\* \ (p\_{j}^{\text{best}} - \text{position} \ (kk)) \\ \text{\\_} &+ \text{inertia\\_cons\\_tax} \ \text{t}\_{2\*} \ \text{positive\\_cons\\_tax} \ \text{t}\_{2} \ ^\* \ \left( G\_{j}^{\text{best}} - \text{position} \ (kk) \right) \end{aligned} \tag{31}$$

$$\text{new position}\_{j}\left(\text{kk}+\text{1}\right) = \text{old position}\_{j}\left(\text{kk}\right) + \text{new velocity}\left(\text{kk}+\text{1}\right) \tag{32}$$

This is because the performance of the ALOA has high iteration to reach the optimal solution and poor performance. To improve the performance of ALOA is based on the PSOA. This property leads to this hybridization of ALOA-PSOA for optimal integration of DG units were identified with intend to reduce the total power losses and total operating cost and enhancement of voltage stability index and the voltage profile at every bus. All the values have been tabulated with different power factor conditions on various test networks with different types of DG units.

The PSOA has still having some issues that ought to be resolved. Therefore, the future works on the PSOA will probably concentrate on the following:

