Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

Jiang, Shouyong and Yang, S. (2017) Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons. IEEE Transactions on Cybernetics, 47 (1). pp. 198-211. ISSN 2168-2267

Full content URL: https://doi.org/10.1109/TCYB.2015.2510698

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Item Type:Article
Item Status:Live Archive


Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for DMOPs with different characteristics. Six representative multiobjective evolutionary algorithms from the literature are investigated based on the proposed DMO test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for DMOPs. © 2016 IEEE.

Additional Information:cited By 16
Keywords:Benchmarking, Multiobjective optimization, Optimization, Pareto principle, Algorithm comparison, Dynamic multiobjective optimization, Evolutionary dynamics, Multi objective evolutionary algorithms, Pareto-optimal front, Performance metrices, Real-world optimization, Time-varying characteristics, Evolutionary algorithms
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35667
Deposited On:01 May 2019 13:02

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