Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning

Xiaolong LIU

doi:10.11999/JEIT201080

Volume 43 Issue 11

Nov. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(11): 3247-3256

Xiaolong LIU. Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3247-3256. doi: 10.11999/JEIT201080

Citation:

Xiaolong LIU. Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3247-3256. doi: 10.11999/JEIT201080

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Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning

doi: 10.11999/JEIT201080

Xiaolong LIU^,

School of Business Administration, South China University of Technology, Guangzhou 510641, China

Funds: The Fundamental Research Funds for the Central University (XYZD201911)

Received Date: 2020-12-25
Rev Recd Date: 2021-03-12

Available Online: 2021-03-24

Publish Date: 2021-11-23

Abstract

Abstract

Compared with traditional swarm intelligence optimization algorithms, the Whale Optimization Algorithm(WOA) has better optimization capabilities and robustness, but there are still problems such as limited global optimization capabilities and difficulty in jumping out of local extremes. Considering the above-mentioned imbalance problem, a multi-group population division idea with vertical and horizontal bidirectional learning is proposed. The subgroups are independent of each other, and the individuals in the subgroups are affected by the optimal values from both the horizontal and vertical directions, thereby avoiding the local optimal and getting the balance between exploration and development.For all individuals in the vertical population, an individual replacement strategy with linearly decreasing probability is proposed to promote the information flow of different subgroups and accelerate the algorithm convergence.The selection of strategy operators is based on the historical evolution information of different individuals, which is different from the existing strategy operator selection methods based on random numbers.The benchmark function is used for cross-document comparison. The numerical results show that the algorithm in this thesis has good superiority and stability. It obtains global extreme on most problems and has good problem applicability.

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References(25)

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