Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms
International Journal of Applied Evolutionary Computation
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In this paper we assess the performance of three modern multiobjective evolutionary algorithms on a real-world optimization problem related to the management of distributed databases. The algorithms assessed axe the Strength Pareto Evolutionary Algorithm (SPEA), the Pareto Archived Evolution Strategy (PAES), and M-PAES, which is a Memetic Algorithm based variant of PAES. The performance of these algorithms is compared using two distinct and sophisticated multiobjective-performance comparison techniques, and extensions to these comparison techniques are proposed. The information provided by the different performance assessment techniques is compared, and we find that, to some extent, the ranking of algorithm performance alters according to the comparison metric; however, it is possible to understand these differences in terms of the complex nature of multiobjective comparisons.