# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.1 RQ1: What Are the Common Bioinspired Algorithms Used for MSA?
The method creates the initial population using Progressive Alignment, which means that a distinct set of parameters is utilized for each territory. The authors have assessed the suggested approach using the BAliBASE benchmark. To tackle the MSA problem, [98] combined earlier research with [PERSON] and [PERSON]'s Quantum Evolutionary approach [102]. Pairwise alignments and the hill climbing process construct the initial population. A Simulated Annealing technique is then used to initialize the quantum population, determining which individuals will be taken to the population. The method was evaluated using the Baliscore quality metric. Another bioinspired-based method is the Simulated Annealing to carry out MSA. It involves adjusting the potential solution, which is governed by a temperature parameter, to investigate the solution space [91]. The methods mostly consist of controlled modifications to the alignment's gap positions. [PERSON] et al. [91] suggested a hybrid method combining Star Alignment and Simulated Annealing to solve the MSA problem. Ultimately, the Multiple Sequence Alignment is obtained by applying this approach. Another bioinspired algorithm applicable to MSA is the Flower Pollination algorithm (FPA) [92; 93]. It is based on the mechanisms of self-pollination and cross-pollination that occur during flower pollination, among others [103]. Each flower represents a potential alignment when used to solve the MSA, and the pollination procedures essentially involve altering the gap positions either globally or locally. To achieve protein MSA, the authors in [92] suggested a novel FPA that involves two main processes: self-pollination and cross-pollination. BAliBASE was utilized in this paper to assess the approach. The outcomes demonstrated that this method might yield to better MSA. But the biological significance of the data was lacking. As a result, the authors suggested expanding on the earlier research by employing a multi-objective, hybrid EPA approach, as shown in [93]; here, the authors evaluated the approach using BAliBASE. Similarly, a multi-objective hybrid ABC was proposed by [PERSON] et al. [15] to carry out MSA. First, each member of the original population is created randomly and referred to as an employed bee to create a new individual. A crossover operator is carried out on a randomly selected bee for each bee engaged during this procedure. The Kalign method [104] establishes a random alignment segment, and the realigned section is returned to its original position. This paper assessed the approach using the BAliBASE benchmark. The authors used PREFAB Q and TC quality indicators. Figure 7 shows the distributions of papers for each category of the bioinspired-based methods for MSA. As can be seen from the figure, the percentage of each of the algorithms identified in the selected article for this SLR is indicated accordingly. The chart shows that the Genetic Algorithm is the most used bioinspired-based algorithm for solving the MSA problem with 48%, followed by the Memetic Algorithm and ABC representing 13% each, of the total papers identified for the MSA solutions. Next are the PSO and FPA with 11% and 5%, respectively, of the total articles identified in the review, followed by the BO and BFO which represent 4% each of the total number of bioinspired-based algorithms  
applied for solving the MSA problem. Finally, we can see that based on the reviewed literature, Simulated Annealing is the least utilized algorithm for MSA with 2% of totally reviewed papers.