Optimization Algorithm Based On Grid Computing Platform For Intelligent Applications And Research

Optimization is a kind of technology, which focuses on the research of what kind of solution is the best and how to find the best solution. With the development of science and technology, a growing number of scholars pay attend to this field, and specially in recent decades, the fast development of computer technology provides effective tools to the optimization and makes it a rapidly improvement.Optimization algorithm is utilized in varied fields such as system control, artificial intelligence, pattern recognition, manufacture schedule, VLSI technology, computer engineering, etc. It plays an important role in the produce course 'optimization, the efficiency improvement, and the saving resource. At the same time, the research of optimization theory is important to improve algorithm performance and widen its application fields. Hence, the research on optimization algorithm is of both theoretic significance and practical value.Based on the grid computing platform ,this paper introduces the theories and applications of some typical intelligent optimization algorithms, including genetic algorithm, simulated annealing, hybrid optimization algorithm, tabu search, hopfield neural network,etc. Different research methods are used based on the character of intelligent optimization algorithms in this paper. For example, the genetic algorithm focuses on the relationship between the optimization results and the parameters, such as generation number, group scale ,etc. The simulated annealing algorithm studies the influence on the optimization results caused by different anneal ways. And the tabu search provides different applications in combination problem and continuum function optimization. The research of hybrid optimization algorithm is also given in this paper. All the studies in this paper have got pretty satisfactory results.This paper consists of three parts. Firstly, it gives a briefly introduction of the grid platform and optimization algorithm. Secondly, from two application instances of TSP problem and Hammerstein pattern recognition , this paper introduces some typical intelligent optimization algorithms' theories , implements and application instances ,and at the same time, gives some comments on these intelligent optimization algorithms and shows the way on how to improve them. At last, this paper gives a summarization and prospect on those intelligent optimization algorithms.