Water wave optimization algorithm(WWO) is a novel population-based optimization algorithm.Despite of its advantages with few controllable parameters,simple operations,and easy implementation,it still risks slow convergence rate and low search precision.To mitigate the aforementioned risks,firstly,a concise yet powerful theoretical analysis was carried out to derive the convergence condition that the control algorithm parameters should satisfy.Then,an improved WWO was proposed to meet the above conditions by incorporating an adaptive algorithm parameters tuning strategy,and it''s expected to further enhance the capability of balancing between global exploration and local exploitation via this strategy.Finally,simulation experiments and statistical comparisons between four algorithms(ApWWO,WWO,FA,MVO) on 10 benchmark functions were conducted.The results show that ApWWO performs significantly better than WWO and FA in terms of search accuracy,speed and robustness,as well as outperforms MVO in five test function.Compared with PSO and GA,ApWWO can yield good performance,which can be affected by the problem''s dimension and population size,and ApWWO also performs well on the permutation flow shop scheduling problem.%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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