Generating quadratic bilevel programming test problems
ACM Transactions on Mathematical Software (TOMS)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Bi-Level Optimisation Using Genetic Algorithm
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Decentralized multi-objective bilevel decision making with fuzzy demands
Knowledge-Based Systems
Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Constructing test problems for bilevel evolutionary multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Bilevel multi-objective optimization problems are known to be highly complex optimization tasks which require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. Multi-objective bilevel problems are commonly found in practice and high computation cost needed to solve such problems motivates to use multi-criterion decision making ideas to efficiently handle such problems.Multi-objective bilevel problems have been previously handled using an evolutionary multi-objective optimization (EMO) algorithm where the entire Pareto set is produced. In order to save the computational expense, a progressively interactive EMO for bilevel problems has been presented where preference information from the decision maker at the upper level of the bilevel problem is used to guide the algorithm towards the most preferred solution (a single solution point). The procedure has been evaluated on a set of five DS test problems suggested by Deb and Sinha. A comparison for the number of function evaluations has been done with a recently suggested Hybrid Bilevel Evolutionary Multi-objective Optimization algorithm which produces the entire upper level Pareto-front for a bilevel problem.