# Bioinspired Algorithms for Multiple Sequence Alignment:
## 1 Introduction  
MSA is a vital and complex task within the field of bioinformatics that plays a crucial role in uncovering the evolutionary connections between biological sequences [1, 2]. While traditional algorithms, such as dynamic programming-based methods [3, 4] can align a limited number of sequences, they struggle with the immense amounts of sequence data generated by modern sequencing technologies. As a result, researchers have turned to innovative, bioinspired algorithms [5] as a potential solution to the challenges of computational efficiency and scalability faced by conventional methods [6].  
Bioinspired algorithms, such as Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC), have been widely used in Multiple Sequence Alignment (MSA) research due to their remarkable ability to unearth optimal or near-optimal solutions [7, 8]. These algorithms mimic natural processes or use population-based strategies, which help them find optimal solutions and avoid becoming stuck in local optima. Due to their adaptability and flexibility, these algorithms are well-suited for addressing the challenges and complexities of the MSA problem [9].  
In the current era, the investigation of bioinspired algorithms for performing Multiple Sequence Alignment continues to be a thriving field of research [10; 11; 12]. This domain constantly evolves with a diverse range of methodologies, including traditional and innovative approaches, such as multi-objective and hybrid techniques [13]. With this in mind, we aim to conduct a thorough systematic literature review (SLR) that concentrates on bioinspired algorithms utilized in MSA. This review provides a methodical and updated view of the state-of-the-art techniques implemented to tackle MSA challenges. We have focused on studies conducted from 2010 to 2024 to ensure the latest advancements are included. Specifically, our study centers on the bioinspired algorithms applied to Multiple Sequence Alignment through a comprehensive analysis of existing research.  
Our goal is to examine the strengths, limitations, and potential areas of improvement in the field of bioinspired algorithms for the MSA problem. By compiling knowledge from various research areas, we aim to deepen our understanding of how these algorithms can enhance the effectiveness and efficiency of MSA techniques. Furthermore, we address the challenges and unresolved issues that persist in this field, paving the way for future research to refine and advance the application of bioinspired techniques in the context of MSA. The key contributions of this paper can be summarized as follows:  
1. Conducting a novel SLR that identifies and summarizes bioinspired-based techniques popularly applied for solving the Multiple Sequence Alignment problem.
2. Recognize and succinctly outline the benchmark datasets employed in evaluating Multiple Sequence Alignment within the framework of bioinspired algorithms.
3. Identify and succinctly summarize the performance evaluation measures utilized for Multiple Sequence Alignment in the context of bioinspired algorithms.
4. Explore and analyze the research challenges, open issues, and future directions for Multiple Sequence Alignment within the context of bioinspired algorithms.  
The structure of this work is as follows: we briefly explain the Multiple Sequence Alignment problem in Section 2. Section 3 presents related recent publications that provide reviews of bioinspired approaches for MSA, highlighting our work's differences and contributions. In Section 4, we provide the methods for conducting the proposed SLR. We present our SLR results in Section 5, emphasizing the figures associated with the publications identified in the SLR. Section 6 provides a view of the limitations of the proposed SLR. In Section 7, we finally give the conclusion and discussion of the paper.