Abstract
In order to efficiently manage the diversity and convergence in many-objective optimization, this paper proposes a novel multi-engine cooperation bacterial foraging algorithm (MCBFA) to enhance the selection pressure towards Pareto front. The main framework of MCBFA is to handle the convergence and diversity separately by evolving several search engines with different rules. In this algorithm, three engines are respectively endowed with three different evolution principles (i.e., Pareto-based, decomposition-based and indicator-based), and their archives are evolved according to comprehensive learning. In the foraging operations, each bacterium is evolved by reinforcement learning (RL). Specifically, each bacterium adaptively varies its own run-length unit and exchange information to dynamically balance exploration and exploitation during the search process. Empirical studies on DTLZ benchmarks show MCBFA exhibits promising performance on complex many-objective problems.
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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), Liaoning PhD startup fund:optimization model and algorithm of power scheduling for microgrid, Postdoctoral fund in China under Grant No. 2016M601332.
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Chen, S., Wang, R., Ma, L., Gu, Z., Du, X., Shao, Y. (2018). A Novel Many-Objective Bacterial Foraging Optimizer Based on Multi-engine Cooperation Framework. 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_49
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DOI: https://doi.org/10.1007/978-3-319-93815-8_49
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