CAJ | 학술논문

分析了量子行为粒子群优化算法，着重研究了算法中的收缩扩张参数及其控制方法，针对不同的参数控制策略对算法性能的影响特点，提出将Q学习方法用于算法的参数控制策略，在算法搜索过程中能够自适应调整选择参数，提高算法的整体优化性能；并将改进后的Q学习量子粒子群算法与固定参数选择策略，线性下降参数控制策略和非线性下降参数控制策略方法通过CEC2005 benchmark测试函数进行了比较，对结果进行了分析。
분석료양자행위입자군우화산법，착중연구료산법중적수축확장삼수급기공제방법，침대불동적삼수공제책략대산법성능적영향특점，제출장Q학습방법용우산법적삼수공제책략，재산법수색과정중능구자괄응조정선택삼수，제고산법적정체우화성능；병장개진후적Q학습양자입자군산법여고정삼수선택책략，선성하강삼수공제책략화비선성하강삼수공제책략방법통과CEC2005 benchmark측시함수진행료비교，대결과진행료분석。
Quantum-behaved Particle Swarm Optimization algorithm is analyzed; contraction-expansion coefficient and its control method are studied. To the different performance characteristics with different coefficients control strategies, a control method of coefficient with Q-learning is proposed. The proposed method can tune the coefficient adaptively and the whole optimization performance is increased. The comparison and analysis of results with the proposed method, con-stant coefficient control method, linear decreased coefficient control method and non-linear decreased coefficient control method based on CEC2005 benchmark function is given.