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

Jiawei Song (), Yang Wang (), Haibo Wang (), Qinghua Wu () and Abraham P. Punnen ()
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Jiawei Song: Huazhong University of Science and Technology
Yang Wang: Northwestern Polytechnical University
Haibo Wang: Texas A&M International University
Qinghua Wu: Huazhong University of Science and Technology
Abraham P. Punnen: Simon Fraser University Surrey

Journal of Heuristics, 2019, vol. 25, issue 4, No 10, 752 pages

Abstract: 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.

Keywords: Multi-wave algorithm; Local search; Adaptive memory; Dispersion problems (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10732-018-9398-5

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