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An Analysis on Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space

An Analysis on Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space

Shi Cheng, Junfeng Chen, Quande Qin, Yuhui Shi
Copyright: © 2018 |Pages: 35
ISBN13: 9781522551348|ISBN10: 1522551344|EISBN13: 9781522551355
DOI: 10.4018/978-1-5225-5134-8.ch006
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MLA

Cheng, Shi, et al. "An Analysis on Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space." Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, IGI Global, 2018, pp. 119-153. https://doi.org/10.4018/978-1-5225-5134-8.ch006

APA

Cheng, S., Chen, J., Qin, Q., & Shi, Y. (2018). An Analysis on Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space. In Y. Shi (Ed.), Critical Developments and Applications of Swarm Intelligence (pp. 119-153). IGI Global. https://doi.org/10.4018/978-1-5225-5134-8.ch006

Chicago

Cheng, Shi, et al. "An Analysis on Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space." In Critical Developments and Applications of Swarm Intelligence, edited by Yuhui Shi, 119-153. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5134-8.ch006

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Abstract

Fireworks algorithms for solving problems with the optima shifts in decision space and/or objective space are analyzed. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single objective problems. The optimum shift in decision space and/or objective space will increase the difficulty of problem solving. Modular arithmetic mapping is utilized in the original fireworks algorithm to handle solutions out of search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies are compared on problems with different dimensions and different optimum shift range. From experimental results, the fireworks algorithms with mapping to the boundary or mapping to limited stochastic region obtain good performance on problems with the optimum shift. This is probably because the search tendency is kept in these two strategies.

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