CAJ | 학술논문

边界处理和全局最优引导者选择操作对多目标粒子群算法的性能有重要影响,在考虑不同操作方法特征的基础上,提出了改进的自适应多目标粒子群(multiobj ective particle swarm optimization,MOPSO)算法。当算法陷入局部最优时,启用交叉变异操作；当算法收敛性停滞时,轮换修剪边界处理和指数分布边界处理操作；当算法多样性停滞时,轮换反比于拥挤距离和反比于控制粒子数目的全局最优引导者概率选择操作。标准测试函数以及柔性交流输电系统(flexible AC transmission system,FACTS)装置优化配置问题的仿真结果验证了所提算法的有效性。
변계처리화전국최우인도자선택조작대다목표입자군산법적성능유중요영향,재고필불동조작방법특정적기출상,제출료개진적자괄응다목표입자군(multiobj ective particle swarm optimization,MOPSO)산법。당산법함입국부최우시,계용교차변이조작；당산법수렴성정체시,륜환수전변계처리화지수분포변계처리조작；당산법다양성정체시,륜환반비우옹제거리화반비우공제입자수목적전국최우인도자개솔선택조작。표준측시함수이급유성교류수전계통(flexible AC transmission system,FACTS)장치우화배치문제적방진결과험증료소제산법적유효성。
Boundary handling and global best guider selection operators play an important role in the multiobj ective particle swarm optimization (MOPSO)algorithm.Considering the characteristics of differ-ent operators,an improved adaptive MOPSO was proposed.When the algorithm falls into a local opti-mum,enable the crossover and mutation operators;when the convergence of algorithm hasn’t improved in a given duration,switch the boundary handling operator between the truncation and the exponential distri-bution truncation methods;when the diversity of algorithm hasn’t improved in a given duration,switch the global best guider selection operator between the probability inverse proportion to the crowding dis-tance and the probability inverse proportion to the number of dominating solutions methods.The results of the benchmark functions and the optimal allocation problem of flexible AC transmission system (FACTS) devices confirm the effectiveness of proposed algorithm.