Article: Recent Advances in Particle Swarm Optimization for Large Scale Problems

Published on: 2019-06-08 | Updated on: 2019-09-02

Journal of Autonomous Intelligence

Content: With the advent of the current era of big data, the scale of real-world optimization problems with many decisive design variables has grown. So far, how to develop new optimization algorithms for these large-scale problems and how to extend the scalability of existing optimization algorithms has presented the further challenges in the field of Biological Heuristic Calculation. Therefore, solving these complex large-scale problems to produce truly useful results is one of the hottest topics at the moment. As a branch of swarm intelligence-based algorithms, Particle Swarm Optimization (PSO) and its wide variety of applications for large-scale problems have grown rapidly over the past decade.

Danping Yan and Yongzhong Lu conducted a study on the large-scale problem particle swarm optimization algorithm, which was published in the journal Autonomous Intelligence. This paper mainly introduces recent research results and trends, and highlights existing unresolved challenges and key issues with significant impact to encourage further research on large-scale PSO theory and its applications in the coming years.

There are still many unresolved problems in spite of the success of large-scale PSO in recent years, which are more likely to have a huge impact on further research progress . For example, the theoretical research of large-scale PSO still lags behind its application, the application of the distribution of particle swarm big data, and the theoretical analysis of the optimal grouping and its features in the covariation framework based on dimensionality reduction. 

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