Journal Article10.1109/TCYB.2015.2510698
Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons
Shouyong Jiang,Shengxiang Yang +1 more
210
TL;DR: A new benchmark generator is proposed 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.
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Abstract: 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.
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Citations
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A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization
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TL;DR: Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.
Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems
TL;DR: A multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP) is presented and it is demonstrated that the proposed algorithm can effectively tackle DMOPs.
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Dynamic Multiobjectives Optimization With a Changing Number of Objectives
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