Soft computing has attracted many research scientists,decision makers and practicing researchers in recent years as powerful computational intelligent techniques,for solving unlimited number of complex real-world prob...Soft computing has attracted many research scientists,decision makers and practicing researchers in recent years as powerful computational intelligent techniques,for solving unlimited number of complex real-world problems particularly related to research area of optimization.Under the uncertain and turbulence environment,classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization.Therefore,new global optimization methods are required to handle these issues seriously.One such method is hybrid Genetic algorithms and Pattern search,a generic,flexible,robust,and versatile framework for solving complex problems of global optimization and search in real-world applications.展开更多
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 h...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.展开更多
文摘Soft computing has attracted many research scientists,decision makers and practicing researchers in recent years as powerful computational intelligent techniques,for solving unlimited number of complex real-world problems particularly related to research area of optimization.Under the uncertain and turbulence environment,classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization.Therefore,new global optimization methods are required to handle these issues seriously.One such method is hybrid Genetic algorithms and Pattern search,a generic,flexible,robust,and versatile framework for solving complex problems of global optimization and search in real-world applications.
文摘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.
