一种自适应参数调整的水波优化算法

Water Wave Optimization Algorithm with Adaptive Parameter Adjustment

水波优化算法(Water Wave Optimization,WWO)是受浅水波理论启发而提出的一种新兴群体智能算法,其具有控制参数少、种群规模小、实现简单、计算开销小等优点,但依然存在局部搜索能力不强、收敛速度较慢等缺陷.首先,通过对水波优化算法在执行全局和局部搜索阶段控制参数变化进行分析的基础上,提出了一种自适应参数调整策略改进的水波优化算法;最后,对改进的算法和包括原WWO、SCA、DA等在内的四种算法在10个标准测试函数上的寻优性能进行试验.结果表明,所提出的策略有效提升了水波优化算法的整体性能,无论在收敛精度还是收敛速度上,改进的水波算法相较于其他三种算法优化结果更加稳定.

Water Wave Optimization （ WWO } is a new kind of swarm intelligence algorithm inspired by the shallow water wave theo- ry- Less control parameters, a small population size,easy implementation and small computational overheads,it also has disadvantages： less strong local search ability,slow convergence speed and so on. First,through the waves in the execution of global and local search optimization algorithm for requirements based on the analysis of the control parameters, improved water wave optimization algorithm used the adaptive adjustment strategy to adjust the control parameters; Finally, for the improved algorithm and including original WWO.SCA.DA, these four kinds of algorithms on 10 standard test functions optimization performance simulation experiment. Results show that the proposed strategy to effectively enhance the water waves to optimize the overall performance of the algorithm, in both the convergence accuracy and convergence speed, improved water wave algorithm, compared with other three kinds of algorithm opti- mization results are more stable.