Abstract
In order to efficiently reduce computational expense as well as manage the diversity and convergence in many-objective optimization, this paper proposes a novel multi-indicator bacterial foraging algorithm with Kriging model (K-MBFA) to guide the search process toward the Pareto front. In the proposed algorithm, a set of preferential individuals for the improved Kriging model are appropriately selected according to the different indicators. Specifically, the stochastic ranking technique is adopted to avoid the search biases of different indicators, which would lead the population to converge to local region of the Pareto front. With several test instances from DTLZ sets with 3, 5, 8 and 10 objectives, K-MBFA is verified to be significantly superior to other compared algorithms in terms of inverted generational distance (IGD).
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Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant No. 6177021519 and No. 61503373 and supported by Fundamental Research Funds for the Central University (N161705001), Shenzhen Science and Technology Innovation Committee (ZDSYS201703031748284).
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Wang, R., Chen, S., Ma, L., Cheng, S., Shi, Y. (2018). Multi-indicator Bacterial Foraging Algorithm with Kriging Model for Many-Objective Optimization. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_50
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DOI: https://doi.org/10.1007/978-3-319-93815-8_50
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