# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.1 RQ1: What Are the Common Bioinspired Algorithms Used for MSA?
The authors evaluated the approach using the BAliBASE benchmark. For MSA, [PERSON] et al. [33] suggested combining a hybrid GA with the Chemical Reaction Optimization (CRO) algorithm. Initially, a starting population is selected at random, the cut point is either an entire column match or a crossover performed with a single-point approach. Following the execution of mutation operators in the GA, the Chemical Reaction Optimization algorithm receives the output. DNA samples from the SWISS-PROT datasets were used to assess the technique. [PERSON] et al. [50] parallelized the GA's primary stage in the MSA-GA tool through multithread programming. The individuals are initially formed using the Needleman-Wunsch algorithm. The authors parallelized the score matrix using the wavefront framework. The authors assessed the technique using the BAliBASE dataset. Based on the NSGA-II, [PERSON] et al. [8] developed a parallel MSA employing a multi-objective GA. Pre-alignments computed by programs like ClustalW [75], MAFFT [37], MUSCLE [76], and T-Coffee are used to create the initial population [73]. Individuals from the original population are processed using crossover and mutation operators to form an auxiliary population. Each individual's fitness is then determined using the STRIKE function, percentage of total columns, and percentage of non-gaps. The approach was created using the BAliBASE benchmark. It is noted, meanwhile, that processing medium- and large-scale datasets presents challenges to the approach. Particle Swarm Optimization (PSO)  
Another bioinspired algorithm used for solving the MSA problem is Particle Swarm Optimization (PSO), which is an approach to solving optimization issues based on a population of particles [77; 78; 79; 80; 81]. The optimization procedure is carried out with each particle's position and velocity considered. Changes are therefore made to these values in accordance with each particle's optimal position as well as the best available global knowledge [82]. Based on the selected studies in this SLR, several PSO-based methods were identified. For example, a PSO algorithm was created by [PERSON] and [PERSON] [65] to generate structurally and evolutionary optimal alignments. Then, each person's fitness is determined using the STRIKE function, percentage of total columns, and percentage of non-gaps. This paper evaluated the approach using the BAliBASE dataset. [PERSON] [83]presented a PSO technique for the MSA problem based on fish swarm intelligence. The plan was to retain the balance between local and global search capabilities while aiming for a faster convergence. The authors of this paper utilized the BAliBASE benchmark to assess the methodology. [PERSON] [20] suggested a two-level PSO algorithm to execute MSA, where each level optimizes a distinct objective function. This tactic works well to stop a locally optimal solution from converging too soon. The authors evaluated the approach using the BAliBASE benchmark. The SP and TC were used as the metrics. Based on earlier research, the authors in [66] introduced a novel two-level PSO technique for RNA sequence-structure alignment. The PSO then executes independently for the two levels. Each alignment's starting length is calculated at level one, and changes are made in the gap places at level two. The tests were conducted using the BAliBASE dataset. We can also observe the application of hybrid PSO. [PERSON] [10] presented a PSO model, the initial step of Simulated Annealing, to carry out MSA that meets the metropolitan criteria. Alignments are produced using the metropolis criterion and are utilized to populate the PSO algorithm's initial population. The approach was evaluated using the BAliBASE dataset. The findings achieved were superior to those of other nature-inspired tools, such as GAPAM [51] and IBBOMSA [84], with an 8 percent rise in the SPS quality rating. A new PSO variation for MSA based on the free electron method and hybridized with HMM was proposed by [PERSON] et al. [85].