A study of two evolutionary/tabu search approaches for the generalized max-mean dispersion problem

Abstract Evolutionary computing is a general and powerful framework for solving difficult optimization problems, including those arising in expert and intelligent systems. In this work, we investigate for the first time two hybrid evolutionary algorithms incorporating tabu search for solving the generalized max-mean dispersion problem (GMaxMeanDP) which has a variety of practical applications such as web page ranking, community mining, and trust networks. The proposed algorithms integrate innovative search strategies that help the search to explore the search space effectively. We report extensive computational results of the proposed algorithms on six types of 160 benchmark instances, demonstrating their effectiveness and usefulness. In addition to the GMaxMeanDP, the proposed algorithms can help to better solve other problems that can be formulated as the GMaxMeanDP.

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