Summary
In this chapter, we generalize dominance relation-based replacement rules in multiobjective optimization. Ordinary two replacement rules based on the dominance relation are usually employed in a local search for multiobjective optimization. One is to replace a current solution with a solution which dominates it. The other is to replace the solution with a solution which is not dominated by it. The movable area in the local search with the first rule is very small when the number of objectives is large. On the other hand, it is too huge to move efficiently with the latter. We generalize these dominance relation-based rules with respect to the number of improved objectives in a candidate solution for local search. We propose a local search unit with the generalized replacement rule, and apply it to existing EMO algorithms. Its effectiveness is shown on knapsack problems and function optimization problems.
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Murata, T., Kaige, S., Ishibuchi, H. (2005). Local Search Direction for Multi-Objective Optimization Using Memetic EMO Algorithms. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_18
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DOI: https://doi.org/10.1007/978-3-540-44511-1_18
Publisher Name: Springer, Berlin, Heidelberg
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