Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons
Dynamic multi-objective optimization 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 dynamic multi-objective optimization problems pose a new challenge to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for dynamic multi-objective optimization, 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), non-monotonic 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 dynamic multi-objective optimization problems with different characteristics. Six representative multi-objective evolutionary algorithms from the literature are investigated based on the proposed dynamic multi-objective optimization test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for dynamic multi-objective optimization problems.
Citation:Yang, S. and Jiang, S. (2016) Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons. IEEE Transactions on Cybernetics, 47 (1), pp. 198-211
Research Group:Centre for Computational Intelligence