CAJ | 학술논문

针对现有的量子粒子群优化算法（ QPSO）中收缩扩张系数α取固定值或线性变化时，不能很好地适应复杂的多维非线性优化搜索问题，提出了两种参数α控制策略：基于Logistic函数的动态非线性递减策略和自适应参数调整策略。在第一种策略中引入S型函数来描述α值在进化过程中的动态变化特性，第二种策略中引入反馈调节方式来控制α值的变化。几个典型函数的实验测试结果表明，两种改进后的参数调整策略对于复杂优化问题在收敛速度和平均最优值上都有所改善，明显优于取固定值或线性变化策略。
침대현유적양자입자군우화산법（ QPSO）중수축확장계수α취고정치혹선성변화시，불능흔호지괄응복잡적다유비선성우화수색문제，제출료량충삼수α공제책략：기우Logistic함수적동태비선성체감책략화자괄응삼수조정책략。재제일충책략중인입S형함수래묘술α치재진화과정중적동태변화특성，제이충책략중인입반궤조절방식래공제α치적변화。궤개전형함수적실험측시결과표명，량충개진후적삼수조정책략대우복잡우화문제재수렴속도화평균최우치상도유소개선，명현우우취고정치혹선성변화책략。
In this paper, two parameter-control methods were proposed to remedy deficiencies of existed Quantum-behaved Particle Swarm Optimization ( QPSO) algorithm based on fixed contraction-expansion coefficientαor linear variation not being able to well address problems in complicated nonlinear optimization search. The first was dynamic nonlinear regressive strategy based on logistic function, in which S-type function was introduced to describe the dynamic nature of the value in its evolvement. The second was the adaptive parameter adjustment strategy, in which feedback regulation was introduced to control change in the value. In the case of complicated optimization, experimental results on several typical functions show that the proposed strategy significantly outperforms the existed ones ( those based on fixed value or linear variation) on both average optimal value and convergence rate.