Abstract
Pareto local optimal solutions (PLOS) are believed to highly influence the dynamics and the performance of multi-objective optimization algorithms, especially those based on local search and Pareto dominance. A number of studies so far have investigated their impact on the difficulty of searching the landscape underlying a problem instance. However, the community still lacks knowledge on the structure of PLOS and the way it impacts the effectiveness of multi-objective algorithms. Inspired by the work on local optima networks in single-objective optimization, we introduce a PLOS network (PLOS-net) model as a step toward the fundamental understanding of multi-objective landscapes and search algorithms. Using a comprehensive set of \({\rho }mnk\)-landscapes, PLOS-nets are constructed by full enumeration, and selected network features are further extracted and analyzed with respect to instance characteristics. A correlation and regression analysis is then conducted to capture the importance of the PLOS-net features on the runtime and effectiveness of two prototypical Pareto-based heuristics. In particular, we are able to provide empirical evidence for the relevance of the PLOS-net model to explain algorithm performance. For instance, the degree of connectedness in the PLOS-net is shown to play an even more important role than the number of PLOS in the landscape.
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References
Borges, P., Hansen, M.: A basis for future successes in multiobjective combinatorial optimization. Technical report, IMM-REP-1998-8, Institute of Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Daolio, F., Liefooghe, A., Verel, S., Aguirre, H.E., Tanaka, K.: Problem features versus algorithm performance on rugged multiobjective combinatorial fitness landscapes. Evol. Comput. 25(4), 555–585 (2017)
Daolio, F., Verel, S., Ochoa, G., Tomassini, M.: Local optima networks and the performance of iterated local search. In: GECCO, pp. 369–376 (2012)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Hoboken (2001)
Doye, J.P.K.: The network topology of a potential energy landscape: a static scale-free network. Phys. Rev. Lett. 88, 238701 (2002)
Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)
Kerschke, P., et al.: Towards analyzing multimodality of continuous multiobjective landscapes. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 962–972. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_90
Knowles, J., Corne, D.: Towards landscape analyses to inform the design of a hybrid local search for the multiobjective quadratic assignment problem. In: Soft Computing Systems, vol. 2002, pp. 271–279 (2002)
Laumanns, M., Thiele, L., Zitzler, E.: Running time analysis of evolutionary algorithms on a simplified multiobjective knapsack problem. Nat. Comput. 3(1), 37–51 (2004)
Liefooghe, A., Derbel, B., Verel, S., Aguirre, H., Tanaka, K.: Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 215–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55453-2_15
López-Ibáñez, M., Liefooghe, A., Verel, S.: Local Optimal sets and bounded archiving on multi-objective NK-landscapes with correlated objectives. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 621–630. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_61
Ochoa, G., Tomassini, M., Verel, S., Darabos, C.: A study of NK landscapes’ basins and local optima networks. In: GECCO, pp. 555–562 (2008)
Ochoa, G., Verel, S., Daolio, F., Tomassini, M.: Local optima networks: a new model of combinatorial fitness landscapes. In: Richter, H., Engelbrecht, A. (eds.) Recent Advances in the Theory and Application of Fitness Landscapes. ECC, vol. 6, pp. 233–262. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-41888-4_9
Paquete, L., Schiavinotto, T., Stützle, T.: On local optima in multiobjective combinatorial optimization problems. Ann. Oper. Res. 156(1), 83–97 (2007)
Richter, H., Engelbrecht, A.E.: Recent Advances in the Theory and Application of Fitness Landscapes. Emergency, Complexity, and Computation. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-41888-4
Tomassini, M., Verel, S., Ochoa, G.: Complex-network analysis of combinatorial spaces: the NK landscape case. Phys. Rev. E 78(6), 066114 (2008)
Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. 227(2), 331–342 (2013)
Acknowledgments
The authors are thankful to Joshua Knowles and Tea Tus̃ar for fruitful discussions relating to this paper. This research was partially conducted in the scope of the MOD\(\bar{\text {O}}\) International Associated Laboratory, and was partially supported by the French National Research Agency (ANR-16-CE23-0013-01).
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Liefooghe, A., Derbel, B., Verel, S., López-Ibáñez, M., Aguirre, H., Tanaka, K. (2018). On Pareto Local Optimal Solutions Networks. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11102. Springer, Cham. https://doi.org/10.1007/978-3-319-99259-4_19
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