Abstract
Brain storm optimization algorithm is a new swarm intelligence algorithm based on the simulation of the human brainstorming process. However, the original BSO may suffer from getting trapped into local optima, due to its fixed search pattern and fixed step-size disturbance mode. Aiming to address this issue, this paper proposes Adaptive Brain Storm Optimization Based on Learning Automata called LABSO to resolve complex optimization problems. The main idea of LABSO is to use the learning automata mechanism to adaptively select optimal parameters and related search strategy for each idea at different search stages of BSO, which can improve the adaptability and flexibility of the algorithm. Especially, the learning automaton engine is apt to select a convergent operation with a large probability in the early search stage, and to choose a divergent operation in the later search stage. The update of the selection probabilities of candidate strategies is not fixed but learned from the evolution process by the learning automaton. Experimental results on a set of CEC2017 benchmark functions have verified the outstanding performance of LABSO in comparison with several representative BSO variants.
Supported by the National Natural Science Foundation of China under Grant No. 61773103, the Fundamental Research Funds for the Central Universities No. N180408019 and N181713002, and the Program for Liaoning Innovative Research Team in University under Grant No. LT2016007.
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Xu, Y., Ma, L., Shi, M. (2020). Adaptive Brain Storm Optimization Based on Learning Automata. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_9
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