## 2 Algorithms And Methods
### System Analysis  
The corresponding steps are explained in Algorithm 1.  
**Step 1:** As an example, let us take mobile nodes that can roam around freely and uniformly in a place that is under the control of a group of heterogeneous networks that are maintained by six individual specialist organizations. The simulation process comprises systems such as CDMA, GSM, WLAN, WiFi, and so on.  
**Step 2:** Number of cuckoo birds: This step takes care of the number of cuckoo birds being taken in the area. Step size: In the process of CSA (cuckoo search algorithm), step size means the distance covered by a cuckoo for a fixed number of loops or iterations. A medium step size is desired to obtain an effective solution. If the step size is very large or very small, it leads to Step 3.  
**Step 3:** This step calls the genetic algorithm and uses the population source that comes from CS (cuckoo search) to find optimum solutions, while the mobile node is busy with other work.  
**Step 4:** At this point, the main optimization plan will choose a group of fitting systems from the accessible systems by considering non-advanced parameter estimations of the considerable number of systems in heterogeneous system conditions. These will be sent to the genetic algorithm, which streamlines the grouping of systems.  
**Step 5:** Therefore, on the off chance that we mimic the same situation with optimized parameters with the quick cuckoo search algorithm, the client can discover the best systems in a sensible length of time, and then identify the best of these systems by using the genetic algorithm.  
**Step 6:** We reduce the time of it takes to find the optimized network using a hybrid approach of cuckoo search with genetic algorithm. This improves latency, bandwidth, PDR, throughput, and handover failure probability.  
**Input:** Network initialization  
```
1:\(Procedure_{Next_{one}}\) (location, destination);
2: Check velocity and position for MN (mobile nodes)
3:\(a\longleftarrow false\)
4:\(Query_{MIIS_{D}BC}\) (location, destination, \(MIIS_{Pkt}\))
5:if\(mit_{cover_{time}}\)(loc, DIS, MIIS.Pkt)\(>0\)then
6:if\(DiS_{RC_{probability}}(loc,DIS,MIIS.Pkt)>0\)then
7:\(a\longleftarrow true\)
8: Initialize the population of host nest return (a) while\(iteration<Max_{network}\)
9:for\(i\)=1 to \(N\)do
10: for (updating fitness functionality by Levy flight)
11: update velocity and position for mobile node
12:\(updateNext_{one}(loc,dest)\)
13: Evaluate threshold (TH)
14: Update fitness function
15: Step 2: Apply genetic fitness function
16: Procedure \(Next_{one}\) (time, prediction window, sense prediction)
17: Evaluate TH
18: Filter network by genetic fitness value
19: Update velocity and position of mobile node
20: Step 3: The hybrid cuckoo search-genetic algorithm phase:
21: Procedure \(check_{next}\) (time, prediction window, sense prediction)
22:for\(i\)=1 to \(N\)do
23: Calculate the probability of crossover and
24: Evaluate TH
25: if converge, then go to step 4
26: else change next position and apply step 2 until converge
27: After convergence find
28: Step 4:TH value \(Cuckoo_{search(GA)}\)
29: Evaluate parameters
30: Calculate \(Cost_{function}\) (TH value)
31: Procedure cost function
32: repeat
33: update application request list (\(MIIS_{weights}\))
34: update list of network preference
35: update QOS, \(QOS_{req}(MIIS_{weights})\)
36:\(TH_{T}H\) (\(app_{req}\), user, QoS)
37: Return
38:endfor
39:endfor
40:endif
41:endif
```  
**Algorithm 1** Proposed Algorithm