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In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high. In this paper is presented new approach for optimization problem with subdivision labeling method (SLM) but in this method for higher dimensional has high computational. SLM Genetic Algorithm (SLMGA) in optimization problems is one of the solutions of this problem. In proposed algorithm the initial population is crossing points and subdividing in each step is according to mutation. RSLMGA is compared with other well known algorithms: DE, PGA, Grefensstette and Eshelman and numerical results show that RSLMGA achieve global optimal point with more decision by smaller generations., Comment: arXiv admin note: substantial text overlap with arXiv:1307.5667, arXiv:1307.5679, arXiv:1307.5838

Optimization problem, nowadays, have more application in all major but they have problem in computation. Calculation of the optimum point in the spaces with the above dimensions is very time consuming. In this paper, there is presented a new approach for the optimization of continuous functions with rotational mutation that is called RM. The proposed algorithm starts from the point which has best fitness value by elitism mechanism. Then, method of rotational mutation is used to reach optimal point. In this paper, RM algorithm is implemented by GA(Briefly RMGA) and is compared with other well- known algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and simulation results show that RMGA achieve global optimal point with more decision by smaller generations., Comment: arXiv admin note: text overlap with arXiv:1307.5534, arXiv:1307.5679, arXiv:1307.5840

In most global optimization problems, finding global optimal point inthe multidimensional and great search space needs high computations. In this paper, we present a new approach to find global optimal point with the low computation and few steps using subdividing labeling method (SLM) which can also be used in the multi-dimensional and great search space. In this approach, in each step, crossing points will be labeled and complete label polytope search space of selected polytope will be subdivided after being selected. SLM algorithm finds the global point until h (subdivision function) turns into zero. SLM will be implemented on five applications and compared with the latest techniques such as random search, random search-walk and simulated annealing method. The results of the proposed method demonstrate that our new approach is faster and more reliable and presents an optimal time complexity O (logn)., Comment: arXiv admin note: text overlap with arXiv:1307.5667, arXiv:1307.5840

Nowadays, optimization problem have more application in all major but they have problem in computation. Computation global point in continuous functions have high calculation and this became clearer in large space .In this paper, we proposed Sub- Dividing Genetic Method(SGM) that have less computation than other method for achieving global points . This method userotation mutation and crossover based sub-division method that sub diving method is used for minimize search space and rotation mutation with crossover is used for finding global optimal points. In experimental, SGM algorithm is implemented on De Jong function. The numerical examples show that SGM is performed more optimal than other methods such as Grefensstette, Random Value, and PNG.

In this paper we use marker method and propose a new mutation operator that selects the nearest neighbor among all near neighbors solving Traveling Salesman Problem.

In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high. In this paper is presented new approach for optimization problem with subdivision labeling method (SLM) but in this method for higher dimensional has high calculation effort. Clustering-Based Parallel Genetic Algorithm (CBPGA) in optimization problems is one of the solutions of this problem. That the initial population is crossing points and subdividing in each step is according to mutation. After labeling all of crossing points, selecting is according to polytope that has complete label. In this method we propose an algorithm, based on parallelization scheme using master-slave. SLM algorithm is implemented by CBPGA and compared the experimental results. The numerical examples and numerical results show that SLMCBPGA is improved speed up and efficiency.

Evaluating a global optimal point in many global optimization problems in large space is required to more calculations. In this paper, there is presented a new approach for the continuous functions optimization with rotational mutation and crossover operator. This proposed method (RMC) starts from the point which has best fitness value by elitism mechanism and after that rotational mutation and crossover operator are used to reach optimal point. RMC method is implemented by GA (Briefly RMCGA) and is compared with other wellknown algorithms such as: DE, PGA, Grefensstette and Eshelman[15,16] and numerical and simulating results show that RMCGA achieve global optimal point with more decision by smaller generations.