# A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease
## 3 Methodology
### Feature Optimization  
The GTO is one of the efficient swarm intelligence-based optimization algorithms, which mimics the behaviour of gorillas. The gorilla group comprises numerous adult female gorillas, an adult male gorilla, and its offspring. The adult male gorilla is a leader, and it directs other gorillas in defending their territory and identifying areas of food abundance [42]. The conventional GTO algorithm comprises two phases, namely the exploitation phase and the exploration phase. The conventional GTO algorithm uses five distinct operators for simulating the optimization process by mimicking the behaviours of gorillas [43]. The exploitation phase uses two operators (competing with adult females and the tracking of silverbacks) for improving the search performance. On the other hand, the exploration phase uses three operators (moving towards a known position, other gorillas' position, and an undiscovered position) for identifying optimal solutions.  
As indicated by [42; 43], the selection of an optimal candidate solution is a fitness function, and it can be analysed using five different evaluation measures, namely specificity, phi coefficient, accuracy, fall-out, and sensitivity, and this optimization algorithm terminates after reaching the maximum number of iterations. In the modified version of the GTO algorithm, a new QRBL process is performed based on the concepts of quasi-opposition-based learning and opposition-based learning to further improve the quality of the optimal position. The quasi-reflection number \(z^{qr}\) of the solution \(z\) is achieved using Equation (7).  
\[z^{qr}=rand\bigg{(}\frac{lb+ub}{2},z\bigg{)} \tag{7}\]  
where \(lb\) is the lower bound, \(ub\) is the upper bound, and \(rand\) is a random number and is uniformly distributed between \(z\) and \(\frac{lb+ub}{2}\). In a D-dimensional space, the quasi-reflection value is extended and mathematically expressed in Equation (8).  
\[z_{i}^{qr}=rand\bigg{(}\frac{lb_{i}+ub_{i}}{2},\ z_{i}\bigg{)} \tag{8}\]  
The parameters that are considered in the MGTO algorithm are a lower bound of 0.2, an upper bound of 0.8, a total iteration of 100, and a population size of 200. This MGTO algorithm selects 8682, 8120, and 9690 features from the ADNI, real-time, and AIBL datasets, which are given into the CapsNet model for image classification. The pseudocode of the MGTO algorithm is depicted in Algorithm 1.  
```
Input: Population size, total iteration, and other parameters Output: Optimal features Initialize the population  Compute the fitness-value While stopping criteria is not met do  Revise the silverback's new position New_position For (\(j\leq\) variables_no) do  Revise the silverback's new position New_position1 End for  Compute fitness-value for New_position and New_position1  When New_position1 is superior to the New_position, substitute it  When New_position is superior to the prior position, substitute it #Exploration-stage For every gorilla do  Update the gorilla's position End for  Compute fitness-value of every gorilla  When the candidate position of the gorilla is superior to the current position, substitute it #Exploitation-stage For every gorilla do  Update the gorilla's position End for #Establishing-group  Compute fitness-value of every gorilla  When the candidate position of the gorilla is superior to the current position, substitute it  Update the silverback's position  Compute the silverback's fitness-value  When 'present new solution' is superior to the 'prior solution', substitute it End while Return: Silverback's with best solution (optimal informative features)
```  
**Algorithm 1** Pseudocode of the MGTO algorithm