Narrow Results

One of the most essential operations in biological sequence analysis is multiple sequence alignment (MSA), where it is used for constructing evolutionary trees for DNA sequences and for analyzing the protein structures to help design new proteins. In this research study, a new method for solving sequence alignment problem is proposed, which is named improved tabu search (ITS). This algorithm is based on the classical tabu search (TS) optimizing technique. ITS is implemented in order to obtain results of multiple sequence alignment. Several variants concerning neighborhood generation and intensification/diversification strategies for our proposed ITS are investigated. Simulation results on a large scale of datasets have shown the efficacy of the developed approach and its capacity to achieve good quality solutions in terms of scores comparing to those given by other existing methods.

The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry. In this paper, a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry. This problem involves 44 variables, 36 constraints, and four decision variables which represent four types of crude oil types. The decision variables have been modeled in the form of fuzzy linear programming problem. The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function. A recursive improved tabu search has been used to solve this fuzzy optimization problem. Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil. The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.