An effective multi-wave algorithm for solving the max-mean dispersion problem

Abstract

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.

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Acknowledgements

We are grateful to the reviewers whose comments have helped to improve our paper. This work was supported by the National Natural Science Foundation of China (Grant No. 71501157).

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Correspondence to Yang Wang.

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Song, J., Wang, Y., Wang, H. et al. An effective multi-wave algorithm for solving the max-mean dispersion problem. J Heuristics 25, 731–752 (2019). https://doi.org/10.1007/s10732-018-9398-5

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Keywords

  • Multi-wave algorithm
  • Local search
  • Adaptive memory
  • Dispersion problems