Abstract
The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: The multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that the GISMOO algorithm can also efficiently solve the multiobjective quadratic assignment problem (mQAP). A performance comparison carried out using well-known published algorithms and shows GISMOO to advantage.
Cite
CITATION STYLE
Zinflou, A., Gagné, C., & Gravel, M. (2013). A hybrid genetic/immune strategy to tackle the multiobjective quadratic assignment problem. In Proceedings of the 12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013 (pp. 933–939). MIT Press Journals. https://doi.org/10.7551/978-0-262-31709-2-ch139
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