JOURNAL OF ALGORITHMS AND COMPUTATION
Year:2019 | Volume:51 | Issue:1|Papers Count:8

Convex hull of some given points is the intersection of all convex sets containing them. It is used as primary structure in many other problems in computational ge-ometry and other areas like image processing, model identi cation, geographical data systems, and triangu-lar computation of a set of points and so on. Comput-ing the convex hull of a set of point is one of the most fundamental and important problems of computational geometry. In this paper a new algorithm is presented for computing the convex hull of a set of random points in the plane by using a sweep-line strategy. The sweep-line is a horizontal line that is moved from top to bottom on a map of points. Our algorithm is optimal and has time complexity O(nlogn) where n is the size of input.

Prediction of heart disease is very important because it is one of the causes of death around the world. More-over, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side e ects it. Ma-chine learning algorithms are able to identify an e ective pattern for diagnosis and treatment of the disease and identify e ective factors in the disease. this paper is in-vestigated a new hybrid algorithm of Whale Optimiza-tion and Dragon y algorithm using a machine learning algorithm. the hybrid algorithm employs a Support Vec-tor Machine algorithm for e ective Prediction of heart disease. Proposed method is evaluated by Cleveland standard heart disease dataset. The experimental re-sult indicates that the SVM accuracy of 88. 89 % and nine features are selected in this respect.

In many industrial and non-industrial applications, it is necessary to identify the largest inscribed rectangle in a certain shape. The problem is studied for convex and non-convex polygons. Another criterion is the direction of the rectangle: axis aligned or general. In this paper a heuristic algorithm is presented for  nding the largest axis aligned inscribed rectangle in a general polygon. Comparing with stare of the art, the rectangles resulted from our algorithm have bigger area. We also proposed an approach to use the algorithm for  finding a rectangle with general direction.

Optimization of inventory costs is the most important goal in industries. But in many models, the constraints are considered simple and relaxed. Some actual con-straints are to consider the combinatorial production and purchase models in multi-products environment. The purpose of this article is to improve the e ciency of inventory management and  nd the economic order quantity and economic production quantity that can minimize the cost of inventory and customer satisfac-tion. In this study, the models with these targets in combinatorial production and purchase systems with the assumption the warehouse and budget constraints are proposed. Since a long time for solving the problem with an exact method is required, we develop a genetic algorithm.

Minimum and maximum operators are two wellknown t-norm and s-norm used frequently in fuzzy systems. In this paper, two different types of fuzzy inequalities are simultaneously studied where the convex combination of minimum and maximum operators is applied as the fuzzy relational composition. Some basic properties and theoretical aspects of the problem are derived and four necessary and sufficient conditions are presented. Moreover, an algorithm is proposed to solve the problem and an example is described to illustrate the algorithm.

In this paper a  nite di erence method for solving 2-dimensional di usion equation is presented. The method employs Crank-Nicolson scheme to improve -nite di erence formulation and its convergence and sta-bility. The obtained solution will be a recursive formula in each step of which a system of linear equations should be solved. Given the speci c form of obtained matri-ces, rather than solving the problem in each step using conventional iterative methods, a closed-form solution is formulated.

Here are many situations in real applications of decision making where we deal with uncertain conditions. Due to the di erent sources of uncertainty, since its original de nition of fuzzy sets in 1965 [45], di erent generaliza-tions and extensions of fuzzy sets have been introduced: Type-2 fuzzy sets [11, 39], Intuitionistic fuzzy sets [1], fuzzy multi-sets [44] and etc. However, in such cases, it is suitable for experts to provide their preferences or assessments by using linguistic information rather than quantitative values.

The explicitly restarted Arnoldi method (ERAM) can be used to  nd some eigenvalues of large and sparse matri-ces. However, it has been shown that even this method may fail to converge. In this paper, we present two new methods to accelerate the convergence of ERAM algo-rithm. In these methods, we apply two strategies for the updated initial vector in each restart cycles. The implementation of the methods have been tested by nu-merical examples. The results show that we can obtain a good acceleration of the convergence compared to orig-inal ERAM.