# Bioinspired Algorithms for Multiple Sequence Alignment:
## 3 Related Work  
The application of bioinspired techniques in Multiple Sequence Alignment (MSA) has garnered considerable attention in the field of bioinformatics, driven by the proliferation of recent scholarly works [31; 32; 33]. Bioinspired algorithms possess robust search capabilities, rendering them well-suited for addressing optimization challenges like MSA [5; 34; 35]. The initial phase entails preparing the sequences for alignment, which encompasses tasks such as sequence preprocessing, selection, and weighting. Subsequently, an objective function is formulated to assess the alignment's quality, typically gauging the similarity or dissimilarity between aligned symbols within the sequences. This objective function may be constructed based on diverse criteria, such as maximizing correspondence or minimizing gaps. Bioinspired algorithms are then employed to optimize the objective function and ascertain the optimal alignment. Leveraging search and optimization methodologies, these algorithms systematically explore the solution space to enhance the quality of the alignment [7; 36].  
Moreover, recent years have witnessed diverse literature appraisals emerging due to the subject's pertinence. For instance, [PERSON] and [PERSON] [24] undertook a metaheuristic survey regarding applications in bioinformatics, elucidating the characteristics of various methods encompassing evolutionary algorithms, Simulated Annealing, and Particle Swarm Optimization. The authors exemplify how these methodologies address computational biology challenges such as MSA, structure prediction, and gene selection. However, it is noteworthy that the authors exclusively concentrate on Genetic Algorithms to address the MSA boundary.  
[PERSON] et al. [6] provided an overview of Multiple Sequence Alignment techniques and significant advancements within the past decade. However, their focus is primarily on illustrating heuristic-based tools such as MAFFT [37; 38] and Clustal [39], among others. [PERSON] et al. [8] contributed two separate comparative studies addressing the application of multi-objective bioinspired algorithms for MSA challenges. The first study delves into the formulation of MSA with three objectives, while the second study offers a more comprehensive examination of generic multi-objective methods for MSA. Nonetheless, instead of adopting a systematic review approach, both articles aim to deliver a thorough experimental assessment of reference techniques in sequence alignment. [PERSON] and [PERSON] [40] reviewed various strategies to address MSA challenges, specifically focusing on Genetic Algorithms as the sole methodologies investigated.  
A recent review by [PERSON] [36] delves into various quality estimation techniques utilized in MSA tools and the fundamental concepts prevalent in sequence alignment research. Furthermore, an assessment of benchmarks for MSA was outlined in [26], concentrating solely on evaluation methods employed for resolving sequence alignments. [PERSON] [31] surveyed nature-inspired algorithms aimed at tackling Multiple Sequence Alignment problems; this article provided an overview of the state-of-the-art nature-inspired algorithms and implemented several algorithms to address MSA challenges, presenting the analyses and results of their experiments. Another review work introduced by [PERSON] [41] focused on parallel computing approaches applied to MSA.  
The principal objective of our present investigation is to introduce a comprehensive systematic literature review aimed at compiling and synthesizing articles pertaining to bioinspired algorithms for Multiple Sequence Alignment. Consequently, our study distinguishes itself from recent reviews that tackle a multitude of biological topics by focusing specifically on this subject matter, as far as our knowledge extends. The Supplementary Materials provide a summary of the pertinent existing reviews in comparison to our study.