Abstract
Hyper-Heuristics is a high-level methodology for selection or generation of heuristics for solving complex problems. Despite their success, there is a lack of multi-objective hyper-heuristics. Our approach, named MOEA/D-HH\(_{SW}\), is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. MOEA/D decomposes a multiobjective optimization problem into a number of subproblems, where each subproblem is handled by an agent in a collaborative manner. MOEA/D-HH\(_{SW}\) uses an adaptive choice function with sliding window proposed in this work to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied by each agent during a MOEA/D execution. MOEA/D-HH\(_{SW}\) was tested in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HH\(_{SW}\) was favourably compared with state-of-the-art multi-objective optimization algorithms.
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References
Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)
Burke, E.K., Silva, J.L., Silva, A., Soubeiga, E.: Multi-objective hyper-heuristic approaches for space allocation and timetabling. In: Meta-heuristics: Progress as Real Problem Solvers, p. 129. Springer (2003)
Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)
Drake, J.H., Özcan, E., Burke, E.K.: An improved choice function heuristic selection for cross domain heuristic search. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 307–316. Springer, Heidelberg (2012)
Gonçalves, R.A., Kuk, J.N., Almeida, C.P., Venske, S.M.: MOEA/D-HH: a hyper-heuristic for multi-objective problems. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9018, pp. 94–108. Springer, Heidelberg (2015)
Kateb, D.E., Fouquet, F., Bourcier, J., Traon, Y.L.: Artificial mutation inspired hyper-heuristic for runtime usage of multi-objective algorithms. CoRR abs/1402.4442 (2014). http://arxiv.org/abs/1402.4442
Kendall, G., Soubeiga, E., Cowling, P.: Choice function and random hyperheuristics. In: Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning, SEAL, pp. 667–671. Springer (2002)
Mashwani, W.K., Salhi, A.: A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Appl. Soft Comput. 12(9), 2765–2780 (2012)
Li, K., Kwong, S., Zhang, Q., Deb, K.: Interrelationship-based selection for decomposition multiobjective optimization. IEEE Transactions on Cybernetics PP(99), 1 (2014)
Maashi, M., Özcan, E., Kendall, G.: A multi-objective hyper-heuristic based on choice function. Expert Systems with Applications 41(9), 4475–4493 (2014)
Sindhya, K., Ruuska, S., Haanpää, T., Miettinen, K.: A new hybrid mutation operator for multiobjective optimization with differential evolution. Soft Comput. 15(10), 2041–2055 (2011)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Vazquez-Rodriguez, J.A., Petrovic, S.: A mixture experiments multi-objective hyper-heuristic. J. Oper. Res. Soc. 64(11), 1664–1675 (2013)
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. Tech. rep., University of Essex and Nanyang Technological University, CES-487 (2008)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Congress on Evolutionary Computation, pp. 203–208 (2009)
Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput. 16(3), 442–446 (2012)
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Gonçalves, R.A., Kuk, J.N., Almeida, C.P., Venske, S.M. (2015). Decomposition Based Multiobjective Hyper Heuristic with Differential Evolution. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_13
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DOI: https://doi.org/10.1007/978-3-319-24306-1_13
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