Abstract
To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. Through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.
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Acknowledgement
This work is supported by National Key R&D Program of China under Grant Number 2022YFB2602200; the National Natural Science Foundation of China under Grant Number 62273263, 72171172 and 71771176; Shanghai Municipal Science and Technology Major Project (2022-5-YB-09); Natural Science Foundation of Shanghai under Grant Number 23ZR1465400; Natural Science Foundation of Shanghai, China under Grant Number 19ZR1479000.
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Ni, W., Guo, W., Li, D. (2025). An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-Learning Pattern for Large-Scale Particle Swarm Optimizers. In: Zhang, H., Li, X., Hao, T., Meng, W., Wu, Z., He, Q. (eds) Neural Computing for Advanced Applications. NCAA 2024. Communications in Computer and Information Science, vol 2181. Springer, Singapore. https://doi.org/10.1007/978-981-97-7001-4_6
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DOI: https://doi.org/10.1007/978-981-97-7001-4_6
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