# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.1 RQ1: What Are the Common Bioinspired Algorithms Used for MSA?
The particles are started randomly, and the PSO is used in the HMM learning process. To spread out the search space, the mean best location is computed. This work assessed protein alignments using the BAliBASE benchmark, while DNA alignments were assessed using a simulated dataset. [PERSON] et al. [67] introduced a novel MSA approach that applies the partition function with a hybrid HMM and PSO technique. The PSO optimizes the HMM parameters, and the distance matrix for the Multiple Sequence Alignment is calculated using the findings. The three well-known protein benchmarks, BAliBASE, QXBench, and SABmark, were utilized to assess the method. This method was later enhanced by [PERSON] et al. [67]. The model was evaluated using other good techniques, such as the MSA generated by these methods, to reconstruct the phylogenetic trees. Memetic Metaheuristic (MA)  
The Memetic Metaheuristic is another bioinspired-based method for conducting MSA [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87]. As an expansion of GA, this metaheuristic employs local search techniques to prevent premature convergence [87]. The Shuffled Frog-Leaping Technique (SFLA) is a well-known memetic algorithm for discrete optimization [100]. Multiple frogs jump in a wetland at SFLA. The frogs communicate with each other to determine which of the limited number of water llies has the greatest food. When frogs are utilized to solve Multiple Sequence Alignment problems, gap locations are adjusted by the operators. A hybridized multi-objective SFLA using Progressive Alignment was proposed by [PERSON] et al. [68]. Initially, a random initial population generator divides the people into subpopulations of water llies, known as memeplexes. The suggested approach was assessed in this work using three benchmarks: BAliBASE, PREFAB, and SABmark. As a result, [PERSON] et al. [88] suggested utilizing OpenMP for distributed computing as a parallel solution to their method. The technique gives each memeplex its own thread, and the outcomes are processed concurrently. Here, the authors assessed the algorithm's performance using the HOMSTRAD dataset. Subsequently, an expansion of the earlier work was published by [PERSON] et al. [89], the primary contribution of which is a compelling parallel version of the method for aligning data sets with a high sequence count. Bacterial Foraging Optimization (BFOA)  
Bacterial Foraging Optimization (BFO) is another essential bioinspired method identified in this SLR. A multi-objective BFO method was presented by [PERSON] and [PERSON] [74] for solving the MSA problem. The population's individuals are first created, and then the chemotaxis phase starts. The BAliBASE benchmark was utilized in this work to assess the methodology. The authors compared the acquired results with other bioinspired-based approaches and commonly used MSA methods. A comparable technique for conducting MSA based on a hybrid BFO and GA was presented by [PERSON] and [PERSON] [99]. Consequently, each individual's fitness is determined, and the top two are chosen for reproduction, which will occur according to a predetermined probability. The suggested approach was assessed using the BAliBASE, PREFAB, SABmark, and OXBench benchmarks. Other Bioinspired Techniques  
In addition to the methods mentioned above, other bioinspired algorithms are identified in this SLR. For example, the authors in [101] introduced Biogeography-Based Optimization for the MSA. The distribution of species across territory serves as the model for this strategy. In this way, through processes known as migration and emigration, species travel between the regions. [PERSON] and [PERSON] [98] presented a novel hybrid method for performing MSA using an optimization algorithm based on biogeography.