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An Optimization Algorithm Based on Brainstorming Process

An Optimization Algorithm Based on Brainstorming Process

Yuhui Shi
Copyright: © 2015 |Pages: 35
ISBN13: 9781466663282|ISBN10: 1466663286|EISBN13: 9781466663299
DOI: 10.4018/978-1-4666-6328-2.ch001
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MLA

Shi, Yuhui. "An Optimization Algorithm Based on Brainstorming Process." Emerging Research on Swarm Intelligence and Algorithm Optimization, edited by Yuhui Shi, IGI Global, 2015, pp. 1-35. https://doi.org/10.4018/978-1-4666-6328-2.ch001

APA

Shi, Y. (2015). An Optimization Algorithm Based on Brainstorming Process. In Y. Shi (Ed.), Emerging Research on Swarm Intelligence and Algorithm Optimization (pp. 1-35). IGI Global. https://doi.org/10.4018/978-1-4666-6328-2.ch001

Chicago

Shi, Yuhui. "An Optimization Algorithm Based on Brainstorming Process." In Emerging Research on Swarm Intelligence and Algorithm Optimization, edited by Yuhui Shi, 1-35. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6328-2.ch001

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Abstract

In this chapter, the human brainstorming process is modeled, based on which two versions of a Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc, which points at one direction for future research on BSO algorithms.

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