计算机科学 ›› 2017, Vol. 44 ›› Issue (1): 253-258.doi: 10.11896/j.issn.1002-137X.2017.01.047
梅海涛,华继学,王毅,文童
MEI Hai-tao, HUA Ji-xue, WANG Yi and WEN Tong
摘要： 为提高求解多目标优化问题的精确性和解集分布的均匀性,提出了一种基于直觉模糊支配的混合粒子群算法。通过引入种群全局目标值标量参数、直觉模糊隶属度和排序方法,定义一种新的最优解支配关系；采用基于模拟退火的Meta-Lamarckian局部学习策略,结合粒子群算法,以避免算法陷入局部最优和早熟；此外,定义种群同构因子来衡量种群多样性,以自适应调节惯性权重和加速因子；提出一种递减扰动策略对粒子飞行速度进行扰动；最后,与多种经典多目标优化算法进行仿真测试比较,结果表明该算法在求解精度、解集分布均匀性上具有明显优势。
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