The Markowitz issue of optimization can’t be solved by precise mathematical methods such as second order schematization, when real world condition and limitations are considered. On the other hand, most managers prefer to manage a small Portfolio of available assets in place of a huge Portfolio. It can be analogized to cardinal constrains, that is, constrains related to minimum and maximum current assets on Portfolios. This study aims to solve the problem of optimizing Portfolios with cardinality constrains, using simulated annealing algorithm. Therefore, by using the information of 50 companies which have been more active in Tehran’s exchange stock from April 2010 to April 2012, Portfolios’ efficient frontier has been supposed from 10 to 50. Results shows that first, simulated annealing algorithm has been successful in solving the above problem, and second, by selecting shares appropriately and determining suitable weights from it, smaller Portfolios with more suitable performances can be selected.

The set covering problem (SCP) is one of the location -allocation models. It s objective is to minimize the cost of facilities location-allocation in an optimal structure in such a way that each customer or zone has been covered by at least one facility. The above problem is a class of NP-Hard problems that cannot be solved by exact algorithms in a reasonable amount of computational time. Thus in this paper, a simulated annealing (SA) algorithm is proposed to solve the SCP. This algorithm is one of the efficient metaheuristics based on a neighbourhood search within solution space, acceptance of probability, and inferior solutions to scape from trap (i.e., local optimal solution). Ten test problems are solved in which associated results show the efficiency and validity: of the SA algorithm for solving the SCP. The validity comparisions of the results obtained the proposed algorithm are compared with the results reported by three methods named as Lingo 8 Software, Heragu's heuristic, and Genetic Algorithms (GAs).

Data clustering is one of the main tasks of data mining, which is responsible for exploring hidden patterns in unlabeled data. Due to the complexity of the problem and the weakness of the basic clustering methods, today most of the studies are directed towards clustering ensemble methods. Although for most datasets, there are individual clustering algorithms that provide acceptable results, but the ability of a single clustering algorithm is limited. In fact, the main purpose of clustering ensemble is to search for better and more stable results, using the combination of information and results obtained from several initial clustering. In this paper, a clustering ensemble-based method will be proposed, which, like most evidence accumulation methods, has two steps: 1-building a simultaneous participation matrix and 2-determining the final output from the proposed participation matrix. In the proposed method, some other information will be used in addition to the clustering of the samples to construct the simultaneous participation matrix. This information can be related to the degree of similarity of the samples, the size of the initial clusters, the degree of stability of the initial clusters, etc. In this paper, the clustering problem is defined as an explicit optimization problem by the mixed Gaussian model and is solved using the simulated annealing algorithm. Also, an evolutionary method based on simulated annealing will be presented to determine the final output from the proposed simultaneous participation matrix. The most important part of the evolutionary method is to determine the objective function that guarantees the final output will be of high quality. The experimental results show that the proposed method is better than other similar methods in terms of different clustering quality evaluation criteria.

In most service and production systems, there is a need of planning and scheduling the multi-shifts work. This paper presents a manpower scheduling problem in order to assign a work pattern (shift-day) considering system and workforce requirements. The objective is to minimize all cost incurred in the proposed mathematical model. This problem belongs to the class of NP-complete ones due to its time complexity function that it cannot be solved by traditional optimization methods in a reasonable computational time. For this reason, a number of heuristic methods have been proposed to solve such a hard problem. In this paper, a heuristic-based simulated annealing is designed and applied. A number of test problems are generated at random and the associated solutions are compared with the Lingo 6 software in order to evaluate and show efficiency of the proposed algorithm.      

The purpose of this study is constitute Index Fund using Simulated Annealing algorithm and Genetic algorithm. Index funds are portfolios which design to decrease the transactions costs to provide return near to the market return by investing in a few number of the index constitutive items. This study is sought to examine the relationship between tracking error and the number of constituent stocks of the fund, also this study will be checked out the performance of the simulated annealing algorithm in comparison of genetic algorithm to form of an index fund. In this study, the priority function will be used for choosing the share of companies that should place in index fund. This priority function selects the shares that have the highest impact on the index, and then by using heuristic algorithms, the efficient weight of these shares will be calculated. The results show that by increasing number of fund shares, the tracking error decreases. Results proved that index fund created by the genetic algorithm in comparison with simulated annealing algorithm had High precision and Superior performance .To create this fund, shares of companies in Tehran stock exchange will be used.