We propose an effective multi-wave algorithm organized in multiple search phases for the max-mean dispersion problem, which offers enhancement of neighborhood search algorithms by incorporating the notion of persistent attractiveness in memory based strategies. In each wave, a vertical phase and a horizontal phase are first alternated to reach a boundary solution. Then a concluding horizontal phase is executed to search around this boundary solution for further solution refinement. Finally, an oscillation phase and a diversified initial solution generation phase focus on search diversification to build well-diversified initial solutions for subsequent waves and passes. Experimental results show that the proposed approach performs quite competitive with state-of-the-art algorithms in the literature. Additional analysis discloses the benefits of the key ingredients in the proposed algorithm.

中文翻译：

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解决最大均值色散问题的有效多波算法

我们针对最大均值离散问题提出了在多个搜索阶段组织的有效多波算法，该算法通过将持久吸引力的概念纳入基于内存的策略中来增强邻域搜索算法。在每个波中，首先将垂直相位和水平相位交替以达到边界解。然后执行最后的水平相位以搜索该边界解，以进行进一步的解细化。最终，振荡阶段和初始解决方案的多样化产生阶段着重于搜索的多样化，以为后续的波和波建立良好的初始解决方案。实验结果表明，该方法与文献中的最新算法相比具有相当的竞争力。