## 3 Proposed Methodology
### Feature Selection MFB-FA (Modified Flashing Behaviour-Based Firefly Algorithm)  
FA (Firefly Algorithm) is stimulated by the FF's (Firefly's) flashing behaviour. Three assumptions exist in FA as follows:  
1. All FFs (Fireflies) are uni-sexual. Each FF gets attracted to each of the other FF. 2. Attraction is proportional to the brightness of FF. For any 2 FFs, the less bright FF will get attracted by the brighter FF, and the brightness will minimize as the distance enhances. Figure 1: Overall view of the proposed system. 3. When no FFs brighter than a specific FF exists, it starts to move in a random manner. Typically, light intensity (LI(r)) is inversely proportional to the distance from the light source. Hence, when the light passes via a medium with the light absorption coefficient of (\(\beta\)), then LI(r) differs with the distance (r) as specified in Equation (1):  
\[\text{LI}(\text{r})=\text{I}_{0}\text{e}^{-\beta\text{dist}^{2}} \tag{1}\]  
In Equation (1), I\({}_{0}\) represents the intensity at the source-point. As the computation of LI(dist) = \(\frac{1}{1+\beta\text{dist}^{2}}\) is simpler than \(\text{e}^{-\beta\text{dist}^{2}}\), LI(r) could be computed as follows:  
\[\text{I}(\text{dist})=\frac{\text{I}_{0}}{\beta\text{dist}^{2}} \tag{2}\]  
Each FF possesses its unique attractiveness (\(\propto\)) and could be claimed as per Equation (3):  
\[\propto(\text{dist})= \propto_{0}\text{e}^{-\beta\text{dist}^{2}} \tag{3}\]  
Likewise, it could be stated as follows:  
\[\alpha(\text{dist})=\frac{\alpha_{0}}{1+\beta\text{dist}^{2}} \tag{4}\]  
In Equation (4), \(\alpha_{0}\) denotes the attraction at dist as 0. A FF positioned at the movement (q\({}_{i}\)) gets attracted to another bright FF positioned at (q\({}_{m}\)) and is given by Equation (5):  
\[\text{q}_{i+1}=\text{q}_{i}+\alpha_{0}\text{e}^{-\beta\text{dist}^{2}}(\text{ q}_{m}-\text{q}_{i})+\zeta\varepsilon \tag{5}\]  
The second term is accredited to attraction, while the third term gets randomization with (\(\zeta(0\leq\zeta\leq 1)\)) and \(\varepsilon\). For numerous practical issues, a constant value (\(\zeta=0.2\)) can be used. In this case, \(\varepsilon\) denotes the random variable vector retrieved from the Gaussian distribution. On the contrary, chaos exists as a stochastic motion mapped by a deterministic equation. It varies from disorder and irregularity. Chaos possesses a fine internal structure. It comprises three characters: random, regularity and ergodic. The ergodic property could search all the states with its formulae within a specific range. Thus, chaos turns into an efficient approach to avoid getting trapped in local optima with the enhancement in determining global optimum. The Gaussian function is given by Equation (6):  
\[\text{q}_{i+1}=\exp\Bigl{(}-\varsigma\text{q}_{i}^{2}\Bigr{)} \tag{6}\]  
From Equation (6), it is revealed that the present study initializes the FF population and interchanges the constant absorption coefficient value with the chaotic maps. In this research, GM (also termed mouse-map) is taken into account for moving all the FFs to global optimal in individual iteration. Further, for stabilizing the movement of a FF, a distinct behaviour is proposed for directing the movement of FFs to the global best solution when no ideal solution around it exists. When a FF is utilized for optimizing the multiple peak function, it could get effortlessly trapped in local minima. This results in less convergence speed. In addition, it is complex for attaining an ideal outcome without using a better search methodology. Chaos is typically a non-linear phenomenon that encompasses features of ergodicity, regularity and randomness as its exquisite internal construction. In this study, MFB-FA was combined with GCM for enhancing the precision and convergence quality of standard FA. Common defects prevailing in FA are discussed below. The evaluation of the defects of FA in its search process is discussed below. 1.