An effective multi-wave algorithm for solving the max-mean dispersion problem
- 111 Downloads
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.
KeywordsMulti-wave algorithm Local search Adaptive memory Dispersion problems
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).
- Aringhieri, R., Cordone, R.: Comparing local search metaheuristics for the maximum diversity problem. J. Oper. Res. Soc. 62, 266–280 (2011)Google Scholar
- Brimberg, J., Mladenović, N., Todosijević, R., Urošević, D.: Less is more: solving the max-mean diversity problem with variable neighborhood search. Inf. Sci. 382, 179–200 (2017)Google Scholar
- Carrasco, R., Pham, A., Gallego, M., Gortázar, F., Martí, R., Duarte, A.: Tabu search for the MaxCMean dispersion problem. Knowl.-Based Syst. 85, 256–264 (2015)Google Scholar
- Glover, F.: Multi-wave algorithms for metaheuristic optimization. J. Heurist. 22, 331–358 (2016)Google Scholar
- Kerchove, C., Dooren, P.V.: The page trust algorithm: how to rank web pages when negative links are allowed? In: Proceedings SIAM International Conference on Data Mining, pp. 346–352 (2008)Google Scholar
- Martí, R., Gallego, M., Duarte, A., Pardo, E.G.: Heuristics and metaheuristics for the maximum diversity problem. J. Heurist. 19(4), 591–615 (2013)Google Scholar
- Mladenović, N., Todosijević, R., Urošević, D.: Less is more: basic variable neighborhood search for minimum differential dispersion problem. Inf. Sci. 326, 160–171 (2016)Google Scholar
- Silver, G.C., Ochi, L.S., Martins, S.L.: Experimental comparisons of greedy randomized adaptive search procedures for the maximum diversity problem. In: Ribeiro, C.C., Martins, S.L. (eds.) Experimental and Efficient Algorithms. Lecture Notes in Computer Science, vol. 3059, pp. 498–512. Springer, Angra dos Reis, Brazil (2004)Google Scholar
- Wang, Y., Hao, J.K., Glover, F., Lü, Z.: A tabu search based memetic search for the maximum diversity problem. Eng. Appl. Artif. Intell. 27, 103–114 (2014)Google Scholar
- Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis, In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354 (2005)Google Scholar
- Yang, B., Cheung, W., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)Google Scholar