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Bilevel Optimization via Collaborations Among Lower-Level Optimization Tasks
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AI
DOI：10.1109/TEVC.2022.3233409.png)
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Abstract
En 中文
Bilevel metaheuristics have been widely used for bilevel optimization. However, recent studies have indicated that most bilevel metaheuristics are inefficient since they perform the lower-level optimization task for each upper-level solution independently and neglect the relationship among lower-level optimization tasks. In this article, we develop a bilevel metaheuristic with the collaborations among lower-level optimization tasks. Specifically, a population is evolved to solve the lower-level optimization tasks for all upper-level solutions collaboratively at each generation. In the population, each solution is associated with a lower-level optimization task. In such a way, all lower-level optimization tasks can be solved in a single run. To capture the individual features of different lower-level optimization tasks, we construct a lower-level search distribution for each lower-level optimization task based on all solutions in the population. In addition, an information-sharing mechanism is proposed to share good solutions among lower-level optimization tasks. Experiments on two sets of test problems and three practical applications demonstrate that our proposed algorithm performs better than other bilevel metaheuristics in comparison.
Keywords:
Task analysis
Metaheuristics
Sociology
Collaboration
Particle swarm optimization
Genetic algorithms
Urban areas
Bilevel optimization
information sharing
metaheuristics
multitask collaboration
search efficiency
Journal
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12
Papers:
1.8K
Citations:
2.4W