Abstract
Bi-level optimization represents a class of optimization problems with two decision levels: the upper level (leader) and the lower level (follower). Bi-level problems have been extensively studied for single objective problems, but there is few research in case of multiobjective problems in both levels. This case is herein studied using a multiobjective particle swarm optimization (MOPSO) based algorithm. To solve the bi-level multiobjective problem the algorithm searches for upper level Pareto optimal solutions. In every upper level search, the algorithm solves a lower level multiobjective problem in order to find a representative set of lower level Pareto optimal solutions for a fixed upper level vector of decision variables. The search in both levels is performed using the operators of a MOPSO algorithm. The proposed algorithm is able to solve bi-level multiobjective problems achieving solutions in the true Pareto optimal front or close to it.
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Carrasqueira, P., Alves, M.J., Antunes, C.H. (2015). A Bi-level Multiobjective PSO Algorithm. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_18
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DOI: https://doi.org/10.1007/978-3-319-15934-8_18
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