In this paper a League Championship Algorithm (LCA) is developed for structural optimization where the optimization variables are of discrete type and the set of the values possibly obtained by each variable is also given. LCA is a relatively new metaheuristic Algorithm inspired from sport Championship process. In LCA, each individual can choose to approach to or retreat from other individuals in the population. This makes it able to provide a good balance between exploration and exploitation tasks in course of the search. To check the suitability and effectiveness of LCA for structural optimization, five benchmark problems are adopted and the performance of LCA is investigated and deeply compared with other approaches. Numerical results indicate that the proposed LCA method is very promising for solving structural optimization problems with discrete variables.

The multi-period portfolio optimization models were introduced to overcome the weaknesses of the single-period models via considering a dynamic optimization system. However, due to the nonlinear nature of the problem and rapid growth of the size complexity with increasing the number of periods and scenarios, this study is devoted to developing a novel League Championship Algorithm (LCA) to maximize the portfolio’ s mean-variance function subject to different constraints. A Vector Auto Regression model is also developed to estimate the return on risky assets in different time periods and to simulate different scenarios of the rate of return accordingly. Besides, we proved a valid upper bound of the objective function based on the idea of using surrogate relaxation of constraints. Our computational results based on sample data collected from S&P 500 and 10-year T. Bond indices indicate that the quality of portfolios, in terms of the mean-variance measure, obtained by LCA is 10 to 20 percent better than those of the commercial software. This sounds promising that our method can be a suitable tool for solving a variety of portfolio optimization problems.

Inverse heat conduction problems, which are one of the most important groups of problems, are often ill-posed and complicated problems, and their optimization process has lots of local extrema. This paper provides a novel computational procedure based on finite differences method and League Championship Algorithm to solve a one-dimensional inverse heat conduction problem. At the beginning, we use the Crank-Nicolson semi-implicit finite difference scheme to discretize the problem domain and solve the direct problem which is a second-order method in time and unconditionally stable. The consistency, stability and convergence of the method are investigated. Then we employ a new optimization method known as League Championship Algorithm to estimate the unknown boundary condition from some measured temperature on the line. League Championship Algorithm is a recently proposed probabilistic Algorithm for optimization in continuous environments, which tries to simulate a Championship environment wherein several teams with different abilities play in an artificial League for several weeks or iterations. To confirm the efficiency and accuracy of the proposed approach, we give some examples for the engineering applications. Results show an excellent agreement between the solution of the proposed numerical Algorithm and the exact solution.

There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League Championship Algorithm is such Algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on League Championship Algorithm for solving large-scale problems. The proposed Algorithm will be used for solving large-scale solving support vector machine model which is a quadratic optimization problem and cannot be solved in a non polynomial time using exact Algorithms or optimally using traditional heuristic ones in large scale sizes. The efficiency of the new Algorithm will be compared to traditional one in terms of the optimality and time measures. The superiority of the Algorithm can be compared versus older version.

In managing a project,reliable prediction is an essential element for success.Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits.For a long time,the earned value management (EVM) for pursuing time performance and the cost of the project has been used.However,using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism.Therefore,the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management.In this framework,using the multi objective League Championship Algorithm (MOLCA),the initial plan of the projects is optimized and then via using the Kalman Filter prediction method,project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers.In this paper,in order to ensure the quality of the solutions,the output of the Algorithm is compared with genetic Algorithms (NSGII) and particle swarm optimization (MOPSO),where results demonstrate the superiority of the proposed Algorithm.

The study was to investigate the feasibility of successful sports teams of East Azerbaijan in the League and the Championship competitions. The method of study was descriptive and causal-comparative and has been done survey way.The Statistical population included individuals specializing in different sports in the province that have points of presence in the League or country Championship. The research sample consisted 155 people were selected from this population by sampling available and purposeful. For gathering the data, the researcher made questionnaires of "Effective Resource of Sports provincial Team's Success in the Competition Championship and country League" was used. Face and content validity of the questionnaire was done based on Lushi model (1975) and content validity of the questionnaire was obtained (CVI= 0.89). Reliability was determined in a pilot study on 30 subjects’ sample (0.81) and the internal reliability for 0.84 total items. We used K-s test with assumptions of normal distribution, to compare variables from Binomial test and Kruskal-Wallis. SPSS software also was used for prioritization and determine the best factors to success of sport teams by TOPSIS method. The results showed that the average respondent comments was significant on the situation of human resources, management, financial, technology, financial, and socio-cultural facilities effect on the success of the provincial teams in the Championship and League Club. The TOPSIS results showed that sociocultural, managerial and material resources ranked first to third resource influences on the success of the provincial teams in the country Championship and human resources, financial, technology and facilities have fourth to seventh ranks. Thus, paying attention to the effective resources of teams and clubs provide success; consultation and cooperation of coaches, officials and athletes would achieve the success goals.

Traders in stock market consider stock information in the past few days as well as the current day information when making decision about selling or buying stock. To imitate stock traders’ style of decision-making, in this article, League Championship Algorithm (LCA) equipped with teams which have network structure has been introduced to extract multi-order rules. Multi-order rules would be extracted by LCA which not only contain the current day information, but also information of the previous days. Thus, a memory to store useful information has been created for each rule. To evaluate the model, 20 shares of companies in different industrial parts of Tehran stock exchange are used. In the testing simulation, the proposed model shows higher profits or lower losses than the buy & hold and genetic network programming models.

This paper addresses a practical but complicated version of Just in Time (JIT) problem in which a set of available jobs with known processing times and due dates are processed on a single machine and delivered in batches of arbitrary size. A new mathematical model is developed to minimize the non-convex sum of earliness-tardiness and delivery costs criteria. Due to the limitations imposed by the large size complexity of the proposed model and its nonlinear nature, we use the recently proposed League Championship Algorithm (LCA) to solve arbitrary test problem instances of the problem on hand. Since LCA works in continuous space, we use several representational schemes to map the solutions generated by LCA to discrete space and compare the output of the Algorithm under each mapping scenario. To measure how effective LCA is in comparison with the mathematical modeling approach and other heuristic methods, we use the Lingo system and a discrete version of the Imperialistic Competitive Algorithm (ICA) as the comparator Algorithms, respectively. Experimental results show that LCA is strongly efficient and dominates the comparator Algorithms. At the same time, the time saved by LCA to report the final output is significant, which recommends the use of this Algorithm for other practical optimization problems.

Increasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer League competition Algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low Algorithm’ s parameters were the motivation of considering the Algorithm in the current research. The proposed ideas to modify the Algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art Algorithms. This comparison shows that the competitive advantages of the proposed Algorithm over the investigated Algorithms.