CAJ | 학술논문

提出一种基于双局部最优的多目标粒子群优化算法，与可行解为优的约束处理方法相结合，来求解决非线性带约束的多目标电力系统环境经济调度问题。该算法针对传统多目标粒子群算法多样性低的局限性，通过对搜索空间的分割归类来增加帕累托最优解的多样性；并采用一种新的双局部最优来引导粒子的搜索，从而增强了算法的全局搜索能力。算法加入了可行解为优的约束处理方法对IEEE30节点六发电机电力系统环境经济负荷分配模型分别在几个不同复杂性问题的情况进行仿真测试，并与文献中的其他算法进行了比较。结果表明，改进的算法能够在保持帕累托最优解多样性的同时具有良好的收敛性能，更有效地解决电力系统环境经济调度问题。
제출일충기우쌍국부최우적다목표입자군우화산법，여가행해위우적약속처리방법상결합，래구해결비선성대약속적다목표전력계통배경경제조도문제。해산법침대전통다목표입자군산법다양성저적국한성，통과대수색공간적분할귀류래증가파루탁최우해적다양성；병채용일충신적쌍국부최우래인도입자적수색，종이증강료산법적전국수색능력。산법가입료가행해위우적약속처리방법대IEEE30절점륙발전궤전력계통배경경제부하분배모형분별재궤개불동복잡성문제적정황진행방진측시，병여문헌중적기타산법진행료비교。결과표명，개진적산법능구재보지파루탁최우해다양성적동시구유량호적수렴성능，경유효지해결전력계통배경경제조도문제。
A two local best based Multi-Objective Particle Swarm Optimization algorithm(2lb-MOPSO) is integrated with superiority of feasible solution constraint handling method in this paper to solve the nonlinear constrained multi-objective Environmental Economic Dispatch(EED) problem. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, the searching space is partitioned into fixed number of bins in the proposed algorithm. The algorithm uses two local best to lead the search particles which can increase the diversity of the population. The algorithm is combined with superiority of feasible solution constraint han-dling method and applied to the standard IEEE 30-bus six-generator test system. The performance is compared against several method obtained from the literature. The results show that the proposed algorithm is able to generate good perfor-mance in terms of both diversity and convergence in solving EED problems.