Abstract
Rich works have been done on brain storm optimization algorithm solving static single- or multi-objective optimization problems, but less reports for dynamic multi-objective optimization problems. Based on this, a grid-based multi-objective brain storming algorithm with hybrid mutation operation is proposed to find the robust Pareto-optimal solution set over time. Grid-based clustering method partitions the objective space evenly along each objective and classifies the individuals located in the same grid into a cluster. Its computational complexity is less than k-means- and group-based clustering strategies. Traditional Gaussian-, Cauchy- and Chaotic-based mutation operators have different mutation steps and generate the new individuals with various diversity. In order to enhance the diversity and avoiding the premature convergence, a hybrid mutation strategy integrating above three mutation operators is presented. Experimental results for eight dynamic multi-objective benchmark functions show that the proposed algorithm can find robust Pareto-optimal solutions approximating the true Pareto front under more subsequent environments with the acceptable fitness threshold. The longer survival time also indicates that grid-based clustering method and hybrid mutation strategy are apt to better robustness.
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Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 61573361, National Key Research and Development Program under Grant 2016YFC0801406 and Six Talent Peaks Project in Jiangsu Province under Grant 2017-DZXX-046.
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Guo, Y., Yang, H., Chen, M. et al. Grid-based dynamic robust multi-objective brain storm optimization algorithm. Soft Comput 24, 7395–7415 (2020). https://doi.org/10.1007/s00500-019-04365-w
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DOI: https://doi.org/10.1007/s00500-019-04365-w