Multiobjective network design for realistic traffic models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Green networks: energy efficient design for optical networks
WOCN'09 Proceedings of the Sixth international conference on Wireless and Optical Communications Networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multicriteria network design using evolutionary algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
A new approach to optical networks security: attack-aware routing and wavelength assignment
IEEE/ACM Transactions on Networking (TON)
Multiobjective network topology design
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
MOEA/D for traffic grooming in WDM optical networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Multiobjective evolutionary algorithms (MOEAs) have attracted growing attention recently. Problem-specific operators have been successfully used in single objective evolutionary algorithms and it is widely believed that the performance of MOEAs can be improved by using problem-specific knowledge. However, not much work have been done along this direction. Taking a network topology planning problem as an example, we study how to incorporate problem-specific knowledge into the multiobjective evolutionary algorithm based on decomposition (MOEA/D). We propose objective-guided operators for the network topology planning problem and use them in MOEA/D. Experiments are conducted on two test networks and the experimental results show that the MOEA/D algorithm using the proposed operators works very well. The idea in this paper can be generalized to other multiobjective optimization problems.