The present paper focus on the improvement of the efficiency of structural optimization, in typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for large scale global optimization...

In this paper, we give a new approach of hybrid direct search methods with metaheuristics of simulated annealing for finding a global minimum of a nonlinear function with continuous variables. First, we suggest a Simple Direct Search (SDS) method, which comes from some ideas of other well known direct search methods. Since our goal is to find global minima and the SDS method is still a local se...

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it necessary to rely on experience skilled workers achieve multiobjective collaborative optimization. The process intelligence perceive parameters cut object through sensors and use machines instead manual decision making. However, traditional sequential algorithm cannot satisfy pr...

In this paper, the author proposes the application of a genetic algorithm and simulated annealing to solve the network planning problem. Compared with other optimisation methods, genetic algorithm and simulated annealing are suitable for traversing large search spaces since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will ...

Task scheduling in an important element a distributed system. It is vital how the jobs are correctly assigned for each computer’s processor to improve performance. The presented approaches attempt reduce expense of optimizing use CPU. These techniques mostly lack planning and need be comprehensive. To address this fault, hybrid optimization technique proposed hybridization both First-Come First...

Clustering is an unsupervised process that divides a given set of objects into groups so that objects within a cluster are highly similar with one another and dissimilar with the objects in other clusters. In this article, a new clustering method based on cat swarm optimization was proposed to find the proper clustering of data sets called K-means improvement and Simulated Annealing selection b...