计算机科学 ›› 2017, Vol. 44 ›› Issue (7): 203-209.doi: 10.11896/j.issn.1002-137X.2017.07.036
刘翱,邓旭东,李维刚
LIU Ao, DENG Xu-dong and LI Wei-gang
摘要： 水波优化算法(Water Wave Optimization,WWO)是最近被提出的一种新型的群智能优化算法。它尽管具有控制参数少、操作简单、容易实现等优点,但是也存在收敛较慢、搜索精度低等不足。针对水波优化算法的不足,首先,从理论上分析并揭示算法收敛时控制参数应满足的条件；然后,提出满足上述条件的改进水波优化算法,改进算法采取自适应机制来调节算法参数,进一步增强了全局探索和局部开发的平衡能力；最后,对4种算法(ApWWO,WWO,FA,MVO)在10个标准测试函数上的寻优性能进行仿真实验和统计比较。结果表明,ApWWO在搜索精度、速度和鲁棒性等方面均显著优于WWO和FA,在5个测试函数上优于MVO；与PSO和GA的对比结果表明,ApWWO具有较好的寻优性能。进一步分析了维数和种群规模对ApWWO的影响,并使用ApWWO来求解置换流水线调度问题,结果表明ApWWO能够取得较好的求解效果。
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