Abstract
In this paper, an improved competitive swarm optimizer (ICSO) for large scale optimization is proposed for the limited global search ability of paired competitive learning evolution strategies. The proposed algorithm no longer uses the competitive winner and the global average position of the current population to update the competitive loser position such a paired competitive learning evolution strategy. Three individuals are randomly selected without returning to compete, the compete failed individual update its speed and position by learning from the other two competing winners, thereby improving the global search ability of the algorithm. Theoretical analysis shows that the randomness of this improved competitive learning evolution strategy has been enhanced. In order to verify the effectiveness of the proposed strategy, 20 test functions from CEC’2010 large-scale optimization test set are selected to test the performance of the algorithm. Compared with the competitive swarm optimization (CSO) and the level-based learning swarm optimization (LLSO) two state-of-the-art algorithms, the experimental results show that ICSO has better performance than CSO and LLSO in solving large-scale optimization problems up to 1000 dimensions.
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Acknowledgment
The work is supported by Hunan Graduate Research and Innovation Project (CX20190807), National Natural Science Foundation of China (Grant Nos. 61603132, 61672226), Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ2137, 2018JJ3188), Science and Technology Plan of China (2017XK2302), and Doctoral Scientific Research Initiation Funds of Hunan University of Science and Technology (E56126).
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Liu, Z., Wu, L., Zhang, H., Mei, P. (2020). An Improved Competitive Swarm Optimizer for Large Scale Optimization. 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_42
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DOI: https://doi.org/10.1007/978-981-15-3425-6_42
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