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In this paper, a modified degree-based strategy is proposed for pinning control of complex networks under constrained control capacity. Limited by the total control strength, the pinning strategy design problem is reconsidered as a constrained optimization problem. To provide a fast and effective solution, a decrease-and-conquer approach has been designed to assign the best control strengths to the pinned highest-degree nodes. Simulation results have demonstrated that the obtained strategy provides an outperformed control effect on various kinds of complex networks, as compared with strategies derived from degree, betweenness or closeness using evenly-distributed control strength. [ABSTRACT FROM AUTHOR]

In this paper, an optimization model with a linear objective function subject to a system of fuzzy relation equations, using max-Hamacher product composition operator, is presented. Since its nonempty feasible solution set is in general a nonconvex set, conventional linear programming methods are not suitable to solve such a problem, so an efficient solution procedure for such problems is necessary. In this paper, the feasible solution set of this problem is studied at first. Then, one efficient algorithm (i.e. tabular method algorithm) is proposed in order to solve the problem. Some procedures are also presented to reduce the original problem. Then, the reduced problem is decomposed (if possible) into several sub-problems with smaller dimensions, so solving them becomes very easier by the algorithm. By combining the algorithm and these procedures, another more efficient algorithm is suggested in order to obtain the optimal solution of the original problem. Some numerical examples are also given to illustrate the algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present a simple lower bound to facilitate the development of branch-and-bound algorithms for the minimization of total completion time in a two-machine flowshop. The studied problem is known to be strongly NP-hard. In the literature, several lower bounds have been proposed. The bounding technique addressed in this paper is based upon a concept about rearrangement of the parameters of the input instance. The technique is intrinsically simple for computer implementations. We conduct computational experiments for problems with 10–65 jobs. Numerical results from our computational study indicate that the new scheme is very effective in reducing the execution time needed for composing optimal solutions. [ABSTRACT FROM AUTHOR]

Earth mover's distance (EMD for short) is a perceptually meaningful dissimilarity measure between histograms. The computation of EMD reduces to a network flow optimization problem; however, it lays a heavy computational burden when the number of locations of histograms is large. In this paper, we address an efficient formulation for computing the exact EMD value. We prove that the EMD problem reduces to a problem with half the number of constraints regardless of the ground distance. We then propose a further reduced formula in which the number of variables is reduced from O(m2) to O(m) for histograms with m locations when the ground distance is derived from a graph with a homogeneous neighborhood structure. Specifically, EMD problems with L1, L∞and D-norm ground distances can be reduced in this manner. Some experiments show that the reduction helps compute the EMD efficiently. [ABSTRACT FROM AUTHOR]

For constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, even if it is difficult to control the penalty parameters. To overcome this shortcoming, this paper presents a new penalty function which has no parameter and can effectively handle constraint first, after which a hybrid-fitness function integrating this penalty function into the objective function is designed. The new fitness function can properly evaluate not only feasible solution, but also infeasible one, and distinguish any feasible one from an infeasible one. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator are also proposed, which can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on ten widely used benchmark problems, and the results indicate the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

During the last years nature inspired intelligent techniques have become attractive for analyzing large data sets and solving complex optimization problems. In this paper, one of the most interesting of them, the Ant Colony Optimization (ACO), is used for the construction of a hybrid algorithmic scheme which effectively handles the Pap Smear Cell classification problem. This algorithmic approach is properly combined with a number of nearest neighbor based approaches for performing the requested classification task, through the solution of the so-called optimal feature subset selection problem. The proposed complete algorithmic scheme is tested in two sets of data. The first one consists of 917 images of pap smear cells and the second set consists of 500 images, classified carefully by expert cyto-technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into seven (7) classes, four (4) representing normal cells and three (3) abnormal cases. Nevertheless, from the medical diagnosis viewpoint, a minimum requirement corresponds to the general two-class problem of correct separation between normal from abnormal cells. [ABSTRACT FROM AUTHOR]

In this paper, the notion of ϵ-strictly efficient solution for vector optimization with set-valued maps is introduced. Under the assumption of the ic-cone-convexlikeness for set-valued maps, the scalarization theorem, ϵ-Lagrangian multiplier theorem, ϵ-saddle point theorems and ϵ-duality assertions are established for ϵ-strictly efficient solution. [ABSTRACT FROM AUTHOR]

In this paper, we present an inventory model for deteriorating items with time varying demand and deterioration rates when the credit period depends on the retailer's ordering quantity. We also provide simple solution procedures for finding the optimal replenishment period and show in a rigorous way that the policy suggested is indeed optimal. Further, we use numerical examples to illustrate the model and conclude the paper with suggestions for possible future research. [ABSTRACT FROM AUTHOR]

In this paper, we study an inventory system with partial backlogging, in which the backlogging rate is a continuous and nonincreasing two piece function. The total shortage cost includes three significant costs: the unit backorder cost (depending on the backorder time), the opportunity cost, and the goodwill cost. This model generalizes several inventory systems studied by other authors. The optimal policy is characterized through several results, which depend on the values of the known input parameters. Illustrative examples and a sensitivity analysis, which help us to understand the theoretical results, are also given. [ABSTRACT FROM AUTHOR]

ϵ-Sensitivity analysis (ϵ-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. In this paper, we discuss the property of ϵ-SA analysis and its relationship with other sensitivity analysis methods. First, we present a new property of ϵ-SA, from which we derive a simplified formula for finding the characteristic region of ϵ-SA. Next, based on the simplified formula, we show that the characteristic region of ϵ-SA includes the characteristic region of Yildirim and Todd's method. Finally, we show that the characteristic region of ϵ-SA asymptotically becomes a subset of the characteristic region of sensitivity analysis using optimal partition. Our results imply that ϵ-SA can be used as a practical heuristic method for approximating the characteristic region of sensitivity analysis using optimal partition. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency and local optimum caused by traditional multi-objective fuzzy optimal methods, the multi-parameter fuzzy optimal mathematical models with improved symmetry and improved asymmetry genetic algorithm based on the overall optimal view are given by considering optimal membership of objectives and constraint. The best optimal solution and optimal constraint value of sub-objective function in the feasible field are found by the mathematical model. The example for helical gear with objective and constraint sets is demonstrated. The algorithm of multi-objective fuzzy optimization is realized by applying the Matlab optimum toolbox. Matlab program to realize multi-parameter fuzzy optimal GA of the helical gear is developed, the best optimal solutions of several fuzzy optimal models is obtained, and the penalty function is used to solve the problem of nonlinear constraint. The result shows that improved fuzzy GA can get satisfying results both in the computing speed and the quality of solution. The method can reflect fuzziness of the problems and practicability of the structure of the engineering project. [ABSTRACT FROM AUTHOR]