基于混合人工鱼群算法的输电网扩展规划

应用于输电网扩展规划的人工鱼群算法(artificial fish school algorithm，AFSA)依靠随机移动无条件接受劣解以摆脱局部极值，具有盲目性大的特点，且该算法一般在优化初期收敛较快而后期收敛速度减慢。针对AFSA的上述缺点，文章结合模拟退火算法，提出一种混合人工鱼群算法(HAFSA)。HAFSA利用模拟退火算法的概率性突跳搜索机制，使局部极值跳跃能力具有可控性，降低了算法的盲目性，提高了算法效率；引入基于分段自适应调整视野策略的反馈机制，兼顾了全局搜索与局部挖掘能力；加入拟遗传算法的变异算子加快了优化后期的收敛速度。通过IEEE6节点和巴西南部46节点算例证明了HAFSA的正确性和有效性。

Transmission Network Expansion Planning Based on Hybrid Artificial Fish School Algorithm

Relying upon random walk of artificial fish when the inferior solution has to be accepted unconditionally, the artificial fish school algorithm (AFSA) applied to transmission network expansion planning gets rid of local extremum, however the defect of this approach lies in its evident blineness, and convergence speed of AFSA is different in optimization stages, in the initial optimization stage its convergence speed is fast and in the later optimization stage the convergence speed decelerates. To remedy above-mentioned defect, combining with simulated annealing algorithm (SA) a hybrid artificial fish school algorithm (HAFSA) is proposed, in which the utilization of probabilistic kick search mechanism of SA makes the skip capability of local extremum controllable, thus the blindness of AFSA algorithm is alleviated and the efficiency of tha algorithm is improved. Lead in the feedback mechanism of the piecewise adaptive adjustment strategy of visual field, both global search ability and local mining ability are considered; adding in the mutation operator of pseudo-genetic algorithm speeds up the convergence speed in later optimization stage. Simulation results of IEEE 6-bus system and Southern Brasilian 46-bus system show that the proposed HAFSA is correct and effective.