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- W4385486548 abstract "Many problems encountered in engineering design and business decision-making can be solved by the bilevel optimization method. There are two optimization tasks in the bilevel optimization problems (BL-MOPs), in which each upper feasible solution corresponds to a lower optimal solution set to a lower-level optimization problem. However, rigidly using nested methods to solve bilevel optimization problems often leads to more evaluations at the upper and lower levels. This paper proposes a grouping-based replication optimization strategy for lower populations for BLMOPs, called BLEMO-PR. Individuals in the upper level of the offspring directly use the lower population of the closest individual to the parent in the upper level as the initial population. To improve the convergence and distributivity of the algorithm, we follow the already proposed dual populations lower-level search strategy. The difference is that when updating the lower-level populations, we use a new iterative selection strategy that considers the neighborhood individuals of non-dominated solutions more likely to be optimal in the next iteration. Experimental results demonstrate that the proposed algorithm maintains better convergence and diversity with fewer evaluations." @default.
- W4385486548 created "2023-08-03" @default.
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- W4385486548 date "2023-01-01" @default.
- W4385486548 modified "2023-10-06" @default.
- W4385486548 title "A Cluster-Based Dynamic Grouping Population Replication Strategy for Bilevel Multi-objective Optimization" @default.
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- W4385486548 doi "https://doi.org/10.1007/978-981-99-4334-0_19" @default.
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