CAJ | 학술논문

提出一种求解复杂电力系统经济负荷分配问题的快速自适应差分进化算法(FSADE).从矢量运算角度对变异算子进行分析,提出了一种改进的变异算子,大大提高了算法的收敛速率.根据个体的进化过程,引入自学习机制,对个体的变异和交叉概率常数进行自适应地调整,提高了算法的鲁棒性.3个不同规模的算例仿真结果表明,与其他4种典型智能优化算法相比, FSADE具有更好的计算精度和计算速度,是一种求解电力系统经济负荷分配问题的有效方法.
제출일충구해복잡전력계통경제부하분배문제적쾌속자괄응차분진화산법(FSADE).종시량운산각도대변이산자진행분석,제출료일충개진적변이산자,대대제고료산법적수렴속솔.근거개체적진화과정,인입자학습궤제,대개체적변이화교차개솔상수진행자괄응지조정,제고료산법적로봉성.3개불동규모적산례방진결과표명,여기타4충전형지능우화산법상비, FSADE구유경호적계산정도화계산속도,시일충구해전력계통경제부하분배문제적유효방법.
@@@@A fast self-adaptive differential evolution algorithm(FSADE) for the complex nonlinear power economic load dispatch problem is proposed. In the view of vector operation, the mutant operator of basic differential evolution algorithm is analyzed, then an improved mutant operator is proposed to improve the convergence speed greatly. According to the individual evolutionary process, a self-learning mechanism is introduced to adapt the mutation constant and crossover probability constant. As a result, the robustness of the proposed algorithm is improved. To demonstrate the effectiveness of the proposed algorithm, three classical test cases are conducted and compared with four other intelligent optimization algorithms. The experiment results show that the proposed FSADE is an very effective algorithm for solving the power economic dispatch.