A New Parallel Chaos Optimization Algorithm with the Number of Variables Reduced
This paper presents a new parallel chaos optimization algorithm with the number of variables reduced. The idea of this algorithm is to use several chaos variables to search in the search space at first. Then reduce the common search space of all chaos variables according to search results and reduce the number of chaos variables according to the size the common search space reduced. After that, continue to search in the search space reduced and repeat the steps above until find the global optimal solution. Taking advantages of parallel chaos optimum algorithm and the convergent method of reducing the common search space of all chaos variables, this algorithm has satisfied global search adequacy, convergent probability and convergent speed. In addition, reducing number of chaos variables dynamically according to the size the common search space reduced can contribute to shorten the running time of algorithm, without influencing adequacy of global search and convergent speed. The results of test functions demonstrate that this algorithm has better optimization performance over other stochastic optimization algorithms and improved chaos optimization algorithms.
parallel chaos optimization search space global search adequacy convergent speed
Zhang Jian Wang Jing
National Engineer Research Center of Advanced Rolling University of Science and Technology Beijing, USTB Beijing China
国际会议
太原
英文
446-449
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）