Baltic J. Modern Computing, Vol. 2 (2014), No. 2, 75-83 An Adaptive Tabu Search Algorithm for Obtaining Alignment of Multiple Sequences Chaabane LAMICHE 1 , Abdelouahab MOUSSAOUI 2 1 Department of Computer Science, Faculty of Mathematics and Informatics, M’sila University, M’sila, Algeria 2 Department of Computer Science, Faculty of Sciences, Setif University, Setif, Algeria lamiche07@gmail.com, moussaoui.abdel@gmail.com Abstract : Multiple sequence alignment (MSA) is one of the most challenging and active ongoing research problems field of computational molecular biology. It is classified as a combinatorial optimization problem, which is solved by using computer algorithms. In this research study, a new method for solving sequence alignment problem is proposed, which is called ATS (Adaptive Tabu Search). This algorithm is based on the classical Tabu Search (TS). ATS is implemented in order to obtain results of multiple sequence alignment. Several ideas concerning neighborhood generation, move selection mechanisms and intensification/diversification strategies for our proposed ATS are investigated. Experiments on a wide range of datasets have shown the effectiveness of the proposed method and its ability to achieve good quality solutions comparing to those given by other existing methods. Keywords: adaptive tabu search, multiple sequence alignment, neighborhood generation, selection mechanism, tabu search.. 1. Introduction Sequence alignment is one of the most important and challenging problems in computational biology and bioinformatics (Karp, 1993). Finding the optimal alignment of a set of sequences is known as a NP-complete problem (Wang and Jiang, 1994). Several iterative methods were proposed in the literature to solve MSA problem. The basic idea is to start by an initial alignment and iteratively refines it through a series of suitable refinements called iterations. The process is reiterated until satisfaction of some criteria. Iterative methods can be deterministic or stochastic, depending on the strategy used to improve the alignment. In this way, several metaheuristics have been designed to obtain suboptimal alignments. Metaheuristics have also been applied to solve this problem, for example, Simulated Annealing (Kim et al., 1994), Tabu Search (Riaz et al., 2004), Ant Colony Algorithm (Chen et al., 2007), Genetic Algorithms (Notredame et al., 1997), among others. The disadvantage is that metaheuristics do not guarantee optimal solutions, but solutions generated can be very close to optimal solution in a reasonable processing time. A brief review of some related works in the multiple sequence alignment field using iterative methods such as tabu search is presented in this section. In (Riaz et al., 2004), authors presented a tabu search algorithm to align multiple sequences. The framework of his work consists to implement the adaptive memory features typical of tabu searches in