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Brain Storm Optimization Algorithms: More Questions than Answers

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Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

Abstract

Brain storm optimization (BSO) algorithms is a framework that indicates algorithms using converging operation and diverging operation to locate the optima of optimization problems. Hundreds of articles on the BSO algorithms have been published in different journals and conference proceedings, even though there are more questions than answers. In this chapter, BSO algorithms are comprehensively surveyed and the future research directions are discussed from the perspective of model-driven and data-driven approaches. For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Many works have been conducted on the BSO algorithms, there are still massive questions on this algorithm need to be answered. These questions, which include the properties of algorithms, the connection between optimization algorithms and problems, and the real-world application should be studied for the BSO algorithms or, more broadly, for the swarm intelligence algorithms.

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Notes

  1. 1.

    If the current particle’s neighborhood includes all particles then this neighborhood best is the global best (termed as gbest), otherwise, it is the local best (termed as lbest).

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant No. 61806119, 61761136008, 61773119, 61703256, and 61771297; in part by the Shenzhen Science and Technology Innovation Committee under grant number ZDSYS201703031748284; and in part by the Fundamental Research Funds for the Central Universities under Grant GK201703062.

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  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Shi Cheng & Xiujuan Lei

  2. School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China

    Hui Lu

  3. Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuhui Shi

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  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Cheng, S., Lu, H., Lei, X., Shi, Y. (2019). Brain Storm Optimization Algorithms: More Questions than Answers. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_1

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