A comparative study of short term scheduling using artificial immune system based techniques and dynamic programming.

摘要

Artificial immune system offers a wide range of research potential because it covers an extremely broad area of applications. Some of these applications are still being studied while some are yet to be discovered by the researchers. Traditionally, this methodology is applicable to life science issues but now it can be adapted to solve multimodal optimization tasks. This research blends artificial immune system into techniques to solve the electrical power system unit commitment problems for short term generator scheduling. The optimization process and result of short-term generators scheduling using artificial immune system algorithm is compared with the reference result of dynamic programming. Clonal selection algorithm, a branch of artificial immune system, is used to perform the optimization. Matlab is employed as the programming mediator. The artificial immune system program is shown to be able to obtain as reliable results as dynamic programming in less computation time.