*3.8.5 Applications*

Cuckoo search optimization algorithm applied for different problems in various domains. Power generations to minimise the cost of flues , n power with probability to generate in different values, Cloud computing security frameworks are-Gathering information, Network mapping, vulnerabilities exploration, audits and penetration tests, vulnerabilities enumeration and categorization, technology selection for vulnerability remediation, security solutions implementation. The security technology is

used to decrease the vulnerability and costs are called Set covering problem [75] that is the Distribution systems will have more power loss and poor voltage regulation and voltage stability. VANET protocols design [76], electromagnetic and antenna arrays [77], classification of IDS [78]. Self-adaptive algorithm for search accuracy of the CSA [79], Compression factor to build [80], dynamic appropriate step-size [81]. CSA have been applied in many researchers in different application problems such as multilevel image thresholding, flood forecasting, wireless sensor networks, data fusion, cluster in wireless networks, clustering, ground water expedition, supplier selection, load forecasting, surface roughness identification, DG allocation in network, BPNN neural network, web service composition, speaker recognition, face recognition, training neural networks [82–85].

#### **3.9 Moth flame optimization (MFO)**

Mirjalili proposed moth flame optimization algorithm a swarm algorithm inspired by movement of moths in spiral path around light source. Moth flames randomly start searching in solution space. The fitness value estimated based on position by each moth in group. Falling category to best position flame by all is optimal solution. The function category updates following spiral movement function to achieve better division towards light source. The best position can be individual positions and repeats updating moth's distance and position generate new position to terminate criteria to be met. The variations in moth flame design in oder to improve are for multi-objective, binary and hybridization

#### *3.9.1 Concept*

Mirjalili proposed meta-heuristic algorithm based on population. MFO moths randomly with in space recognize fitness value and identify position suitable without flame. The movement is continuous and repeated to recognize better position. Update position suitably until termination criteria is met. The process MFO is carried on in three main steps. In first step, initialization of population and parameters are assumed in hyper dimensional space. The difference in way updates and treats in iterations. The position of each moth is stored. The selection of best moth is also performed so that results are stored longer time. In second step, three main functions converge to global result in Eq. (46). The identification to optimization is implemented randomly. Movement is spiral in moths applying logarithmic spiral function by Eq. (47). Moth and flame fixed position and indicate [�1, 1] ranges. It balances between exploitation and exploration to guarantee moths circulation in search space guarantee in spiral motion. The fly of moth is traps of the local optima. Moth positioned near flame represented in matrix. In step 3, number of flames is updated; Moths locations search the exploitation in search space. Decrease and solve issue based on Eq. (48).

$$M(i,j) = (ub(i) - lb(j) \* rand() + lb(i))\tag{46}$$

$$S(M\_i, F\_j) = D\_i e^{bt}. \cos \left(2\pi t\right) + F\_j \tag{47}$$

$$f \text{flamecount} = |N - l \ast \frac{N - l}{T}| \tag{48}$$

*Bio-inspired Optimization: Algorithm, Analysis and Scope of Application DOI: http://dx.doi.org/10.5772/intechopen.106014*

### *3.9.2 Algorithm and flowchart*

The algorithm and flow of operations of MFOA is presented in **Figure 11**.
