Abstract
The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. In this paper, brain storm optimization in objective space (BSO-OS) algorithm is utilized to solve multimodal optimization problems. Our goal is to measure the performance and effectiveness of BSO-OS algorithm. The experimental tests are conducted on eight benchmark functions. Based on the experimental results, the conclusions could be made that the BSO-OS algorithm performs good on solving multimodal optimization problems. To obtain good performances on multimodal optimization problems, an algorithm needs to balance its global search ability and solutions maintenance ability.
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Acknowledgement
The research work reported in this paper was partially supported by the National Natural Science Foundation of China under Grant Number 61273367, 61403121, and 71402103
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Cheng, S., Qin, Q., Chen, J., Wang, GG., Shi, Y. (2016). Brain Storm Optimization in Objective Space Algorithm for Multimodal Optimization Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_47
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DOI: https://doi.org/10.1007/978-3-319-41000-5_47
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