TY - GEN
T1 - mBSO : A Multi-Population Brain Storm Optimization for Multimodal Dynamic Optimization Problems
AU - POURPANAH, Farhad
AU - WANG, Ran
AU - WANG, Xizhao
AU - SHI, Yuhui
AU - YAZDANI, Danial
N1 - This work is partially supported by the National Natural Science Foundation of China (Grant nos. 61772344, 61761136008, 61811530324 and 61732011), the Natural Science Foundation of SZU (Grant nos. 8270 0 0140, 827-0 00230, and 2017060), and the Interdisciplinary Innovation Team of SZU.
PY - 2019/12
Y1 - 2019/12
N2 - Brain Storm Optimization (BSO), which is an effective swarm intelligence method inspired by the human brainstorming process, has shown promising results in solving static optimization problems. However, The search spaces of many real-world problems change over time, in which the original BSO and its variants are not able to cope with. This paper extends BSO as an adaptive multi-population based algorithm, i.e., mBSO, to solve dynamic optimization problems (DOPs). Firstly, a modified BSO, which uses new update mechanisms independent from the maximum number of iterations and objective space grouping method, is proposed. Then, the modified BSO is embedded in a multi-population framework. Several mechanisms such as convergence detection, exclusion, and re-diversification are employed to address the challenging issues of DOPs. The moving peaks benchmark (MPB) is used to evaluate the performance of mBSO along with comparison with other state-of-the-art methods. The outcome indicates the efficiency of the proposed mBSO in locating optima and tracking them after environmental changes.
AB - Brain Storm Optimization (BSO), which is an effective swarm intelligence method inspired by the human brainstorming process, has shown promising results in solving static optimization problems. However, The search spaces of many real-world problems change over time, in which the original BSO and its variants are not able to cope with. This paper extends BSO as an adaptive multi-population based algorithm, i.e., mBSO, to solve dynamic optimization problems (DOPs). Firstly, a modified BSO, which uses new update mechanisms independent from the maximum number of iterations and objective space grouping method, is proposed. Then, the modified BSO is embedded in a multi-population framework. Several mechanisms such as convergence detection, exclusion, and re-diversification are employed to address the challenging issues of DOPs. The moving peaks benchmark (MPB) is used to evaluate the performance of mBSO along with comparison with other state-of-the-art methods. The outcome indicates the efficiency of the proposed mBSO in locating optima and tracking them after environmental changes.
KW - Brain storm optimization
KW - dynamic environments
KW - moving peaks benchmark
KW - multi-population
KW - multimodal dynamic optimization
UR - http://www.scopus.com/inward/record.url?scp=85080962347&partnerID=8YFLogxK
U2 - 10.1109/SSCI44817.2019.9002850
DO - 10.1109/SSCI44817.2019.9002850
M3 - Conference paper (refereed)
AN - SCOPUS:85080962347
SN - 9781728124865
T3 - IEEE Symposium Series on Computational Intelligence (SSCI)
SP - 673
EP - 679
BT - Proceedings of 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PB - IEEE
T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Y2 - 6 December 2019 through 9 December 2019
ER -