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A novel modified gravitational search algorithm for the real world optimization problem

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Abstract

The directing orbits of chaotic systems is a common multimodal optimization problem in the engineering field. However, when this multimodal optimization problem is solved by evolutionary algorithm, it is difficult for the method to obtain the high-quality solution for easily falling into a local optimal solution. To address this concerning issue, a novel global gravitational search algorithm with multi-population mechanism (named GGSA) is proposed. GGSA makes use of the clustering method to divide the whole population into several subpopulations for maintaining the population diversity. Then, the information contained in global best agent is used to update the current agent for improving the convergence speed. By this way, the proposed algorithm can achieve a right tradeoff between the exploration and the exploitation. Finally, the directing orbits of discrete chaotic systems are used to test the performance of the proposed algorithm. The experimental results show GGSA has better performance than other compared methods.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grant 61772391, Grant 61402534, Grant 71771208 and Grant 71271202, in part by Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ6051 and Grant 2017JQ6059, in part by Young Talent fund of University Association for Science and Technology in Shaanxi, China, and in part by the Fundamental Research Funds for the Central Universities under Grants JB160708 and XJS18009.

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  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, Shannxi, China

    Lingling Huang & Chuandong Qin

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  1. Lingling Huang
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  2. Chuandong Qin
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Correspondence to Lingling Huang.

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Huang, L., Qin, C. A novel modified gravitational search algorithm for the real world optimization problem. Int. J. Mach. Learn. & Cyber. 10, 2993–3002 (2019). https://doi.org/10.1007/s13042-018-00917-y

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