Metabolomics ISSN:2153-0769 JOM an open access journal Conference Proceedings Open Access Unay and Guzey, Metabolomics 2013, 3:2 DOI: 10.4172/2153-0769.1000120 Volume 3 • Issue 2 • 1000120 Particle swarm optimization In the feld of computer science, PSO is a method that aims to optimize a problem by trying to improve a possible solution with iteration in limitation and manipulation of a pre-defned quality measure. PSO processes an initial population of possible solutions, dubbed particles in this case, and changing the position of these particles in the search-space according to mathematical formula which is consisting of the particles’ (a) position and (b) velocity. An individual particle’s movement is altered according to its “local best known position” and is also manipulated toward the “best known positions” in the search-space. Te best known positions are updated as positions which are more satisfactory for quality criteria, are discovered by other particles. Tis mode of action is supposed to conduct the movement of the swarm toward the best solutions [5]. PSO was frst intended for simulating social behavior, as a stylized representation of the movement of organisms in a bird fock or fsh school [6,7]. Te algorithm was simplifed and it was observed to be performing optimization. Particle swarm optimization on longest common subsequence problem Tis study uses PSO heuristic technique on LCSP. First, the algorithm will take n sequences and generate an alphabet among all of the distinct sequence elements without uncommon elements. Ten it will generate a population of random sequences of the alphabet. Every sequence will be a particle. It will do the evaluation with the technique, occurrence evaluation, which will be described in detail in this paper. Afer the evaluation, known local best score will be compared with the global best score. If local best score is bigger, then it is the new global best (initial global best is 0). Afer this, each particle move towards to *Corresponding author: Meral Guzey, Department of Math and Life Sciences, Main campus of University Maryland University College (UMUC), USA, E-mail: meral.guzey@faculty.umuc.edu Received June 25, 2013; Accepted August 06, 2013; Published August 13, 2013 Citation: Unay AT, Guzey M (2013) A Swarm Intelligence Heuristic Approach to Longest Common Subsequence Problem for Arbitrary Number of Sequences. Metabolomics 3: 120. doi:10.4172/2153-0769.1000120 Copyright: © 2013 Unay AT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. A Swarm Intelligence Heuristic Approach to Longest Common Subsequence Problem for Arbitrary Number of Sequences Ali Teoman Unay 1 and Meral Guzey 2 1 Department of Intelligent Computing Systems, İzmir University of Economics, İzmir, Turkey 2 Department of Math and Life Sciences, Main campus of University Maryland University College (UMUC), USA Introduction In the clinical diagnosis of cancer, the corresponding biomarker methods and measurements are based on the correct algorithms, which are used for sequencing. Te necessity of development new or possibly re-establishing previous biomarkers in the feld of cancer research initiated us to work on new sequencing techniques. Here, we describe “Longest Common Subsequence Problem” (LCSP) with Particle Swarm Optimization (PSO), with the proposal of novel “Occurrence Listing” (OL) technique as an evaluation function. Previous studies shows, that despite the wide diferences between popular approaches, like dynamic programming or other heuristic methods, even there are some variations of dynamic programming for three or more sequences [1], they generally work on two inputs (sequences) [2,3]. Te benefts of our system are as follows: First, one can work with minimum two or more sequences. Second, one has fexibility of working with arbitrary number of sequences. Longest common subsequence problem LCSP dwells on longest common subsequence of two or more sequences. Although general case of a random number of input sequences, the problem is NP-Hard, Dynamic programming can manage to solve the problem on polynomial time provided that the number of is constant [4]. What is “subsequence”? A subsequence is a sequence that is created from another sequence by excluding some elements but without changing the initial order. For example, <A,B,D> sequence derived from <A,B,C,D,E,F> by omitting element C, E and F. Given two sequences X and Y, sequence G can be defned as a common subsequence of X and Y, if G can be derived from both X and Y individually. For example, if X=<A,B,C,D,E,G,C,E,D,B,G> and Y=<B,E,G,C,F,E,U,B,K> then a common subsequence of X and Y could be G=<B,E,E> Tis would not be the longest common subsequence. Te longest common subsequence of X and Y is <B,E,G,C,E,B>. Abstract Personalized cancer care strategies involving sequencing requires accuracy. We aimed to develop a novel approach to solve the longest common subsequence problem, which is a common computer science problem in the feld of bioinformatics to facilitate the next generation sequencing of cancer biomarkers. We are using particle swarm optimization heuristic technique, which uses a novel “Occurrence Listing” (OL) technique as the evaluation function. This aims to keep lists of the sequence elements and offers criteria to evaluate randomly generated population of sequences. M e t a b o l o m i c s : O p e n A c c e s s ISSN: 2153-0769 Metabolomics: Open Access