High-Dimensional Function Optimization with a Self Adaptive Differential Evolution
A good optimization algorithm must be capable of handling high-dimensional problems, meaning that there are many decision variables to be optimized at the same time. The problems of this category are challenging. This paper tests the scalability of wDE, which is a differential evolution algorithm with self-adaptive parameters. The statistical results and convergence graphs from the experimentation using benchmark problems of 100-, 500-, and 2000-dimensions are analyzed and compared to three standard variants of differential evolution algorithm.
Differential Evolution Evolutionary Algorithm High dimensional Scalability Self-Adaptation.
Chukiat Worasucheep
Department of Mathematics,Faculty of Science,King Mongkuts University of Technology Thonburi,Thailand.
国际会议
上海
英文
668-673
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）