摘要
机组组合是一个大规模混合整数规划问题,具有高维、离散、非线性等特点,在数学上被称为NP-hard问题。国内外研究表明蚁群算法在解决组合问题时有其特有的优越性。提出的多种群混沌蚁群算法在基本蚁群算法的基础上,把蚁群分为搜索蚁、侦察蚁和工蚁,并引入了混沌量。一方面继承了蚁群算法在解决组合问题上的优越性;另一方面最大限度地克服蚁群算法本身的运算速度慢、易陷入局部最优等缺点。最后用修正后的IEEE30节点系统对算法可行性作了验证,并对算法的合理性和有效性进行了分析。结果表明,所提出的多种群蚁群算法是合理有效的。
Unit commitment （UC） has commonly been formulated as a large-scale mixed-integer optimization problem with the characteristic of being high-dimensional, discrete and nonlinear and is known as NP-hard problem in Mathematics. Domestic and international studies show that ant colony algorithm has its unique advantages in solving combination problems. The multi colony chaotic ant optimization algorithm presented in this paper bases on the basic ant optimization algorithm, divides the ant colony into search ant, detect ant and ergate, and brings in the chaotic volume. On one hand the algorithm inherits the superiority of ant colony algorithm in solving combination problems; on the other hand, the new algorithm maximizes the possibility for ant colony algorithm to overcome its slow operation, easy to fall into local optimum and other shortcomings. Finally, we verify the feasibility of the algorithm by the modified IEEE30, and the rationality and effectiveness of the algorithm are analyzed. The results show that the proposed multi colony ant algorithm is reasonable and effective.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2012年第9期13-17,共5页
Power System Protection and Control
关键词
机组组合
多种群蚁群算法
混沌
启发式算法
经济调度
unit commitment
multi colony ant optimization algorithm
chaos
heuristic algorithm
economic dispatch
