# Bioinspired Algorithms for Multiple Sequence Alignment:
## 4 Methods
### Synthesis Results
#### 5.2.1 RQ1: What Are the Common Bioinspired Algorithms Used for MSA?
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Description** & **References** \\ \hline GA & \begin{tabular}{c} An evolutionary algorithm based on natural selection and genetics is \\ utilized to find approximate solutions to optimization and search \\ problems. \\ \end{tabular} & [12; 21; 22; 33; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64] \\ PSO & \begin{tabular}{c} A population-based stochastic method that simulates the movement and \\ cooperation of particles inspired by the social behavior of birds and fish. \\ \end{tabular} & [10; 20; 65; 66; 67] \\ Memetic Algorithm & \begin{tabular}{c} A technique that blends local search methods with evolutionary \\ algorithms, such as genetic algorithms. \\ \end{tabular} & [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] \\ Simulated Annealing & \begin{tabular}{c} A technique for probabilistic optimization that draws inspiration from the \\ metallurgical annealing process. \\ \end{tabular} & [91] \\ The Flower & \begin{tabular}{c} An algorithm that mimics the natural process of pollination in blooming \\ plants, in which pollen is spread from one flower to another and fertilizes \\ \end{tabular} & [92; 93] \\ Pollination Algorithm & \begin{tabular}{c} the plants. \\ \end{tabular} & [14; 94; 95; 96; 97] \\ Artificial Bee & \begin{tabular}{c} A method for population-based optimization influenced by honeybee \\ foraging. \\ \end{tabular} & [14; 94; 95; 96; 97] \\ Colony & \
\begin{tabular}{c} An optimization algorithm inspired by nature that is grounded in \\ biogeography, the study of species distribution across various geographic \\ regions. \\ \end{tabular} & [84; 98] \\ Biacterial &
\begin{tabular}{c} A nature-inspired optimization algorithm that is inspired by the foraging \\ of bacteria. \\ \end{tabular} & [74; 99] \\ Optimization & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the bioinspired algorithms for MSA. values are computed for each individual. A modified version of the consistency-based COFFEE goal function replaces the Weighted Sum-of-Pairs technique. The approach was evaluated using the BAliBASE benchmark by applying the REFAB Q metric in DNA sequence alignment. To obtain decent results, [PERSON] and [PERSON] [74] suggested an additional GA that concentrated on crossover operators. The first population is created randomly, and then, a multi-objective method that computes the sum-of-pairs and total column scores for each individual is used, then an elimination criterion is implemented. The authors used each test case for the BAliBASE datasets to evaluate the methodology. [PERSON] et al. [48] developed a multi-objective GA for multiple RNA sequence alignment based on Pareto Optimality. The original population is randomly initialized, the non-dominant individuals are cloned into an archive population, and each individual's fitness is calculated. The authors assessed this approach using the BAliBASE benchmark and SPSS and SCI metrics. The authors noted that the results are highly sensitive to the gap and mutation factors and recommended varying these parameters in subsequent research. In a different study, the RNA alignment evaluation approach using a multi-objective GA with dynamic weight was proposed by [PERSON] et al. [48]. The starting population is created randomly, and each individual is assessed for fitness using a multi-objective method that includes base pam score weighted completely matched column and entropy. The approach has been assessed using the BAliBASE benchmark. The model uses SPSS and SCI as the evaluation metrics. Based on NSGA-II, [PERSON] et al. [49] presented a multi-objective GA for MSA. Three objective functions were used to calculate the fitness of the initial population, which is recreated at random: support maximization, affine-gap penalty, and similarity.