Abstract
Suppose two solution vectors are needed that have good objective function values and are different from each other. The following question has not yet been systematically researched: Should the two vectors be generated sequentially or simultaneously? We provide evidence that for broad ranges of practically achievable distances, sequential generation usually requires less computational effort and produces solutions that are at least as good as produced by simultaneous generation. This is done using experiments based on publicly available instances of the multi-constrained, zero-one knapsack problem, which are corroborated using experiments conducted with the linear assignment problem.
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References
Beasley, J.E.: http://people.brunel.ac.uk/~mastjjb/jeb/orlib/mknapinfo.html (2005)
Burkard, R., Çela, E.: Linear assignment problems and extensions. In: Du, D.-Z., Pardalos, P. (eds.) Handbook of Combinatorial Optimization, pp. 75–149. Kluwer Academic, Dordrecht (1999)
Ehrgott, M., Gandibleux, X. (eds.): Multiple Criteria Optimization: State of the Art Bibliographic Surveys. Kluwer Academic, Boston (2002)
Fréville, A.: The multidimensional 0–1 knapsack problem: an overview. Eur. J. Oper. Res. 155, 1–21 (2004)
Fu, Z., Golden, B., Lele, S., Raghavan, S., Wasil, E.: Genetically engineered decision trees: population diversity produces smarter trees. Oper. Res. 51, 894–907 (2005)
Glover, F.: Scatter search and star paths: beyond the genetic metaphor. OR Spektrum 17, 125–137 (1995)
Greistorfer, P., Voß, S.: Controlled pool maintenance for metaheuristics. In: Rego, C., Alidaee, B. (eds.) Metaheuristic Optimization via Memory and Evolution, pp. 387–424. Kluwer Academic, Boston (2005)
Løkketangen, A., Woodruff, D.L.: A distance function to support optimized selection decisions. Dec. Support Syst. 39, 345–354 (2005)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program, pp. 158–179. California Institute of Technology, Pasadena, CA 91125, U.S.A., C3P Report 826 (1989)
Ryu, T., Eick, C.F.: A unified similarity measure for attributes with set or bag of values for database clustering. In: Proc. Sixth International Workshop on Rough Sets, Data Mining and Granular Computing (RSDMGrC’98) (1998)
Sörensen, K., Sevaux, M.: MA|PM: memetic algorithms with population management. Comput. Oper. Res. 33, 1214–1225 (2006)
Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation and Application. Wiley, New York (1986)
Tversky, A.: Features of similarity. Psychol. Rev. 84, 327–352 (1977)
Whitley, D.: The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 116–121. Morgan Kaufmann, San Mateo (1989)
Zeleny, M.: Multiple Criteria Decision Making. McGraw–Hill, New York (1982)
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Greistorfer, P., Løkketangen, A., Voß, S. et al. Experiments concerning sequential versus simultaneous maximization of objective function and distance. J Heuristics 14, 613–625 (2008). https://doi.org/10.1007/s10732-007-9053-z
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DOI: https://doi.org/10.1007/s10732-007-9053-z