[1]ZHAO Shijie,ZHANG Tianran,MA Shilin,et al.Improved barnacles mating optimizer to solve high-dimensional continuous optimization problems[J].CAAI Transactions on Intelligent Systems,2023,18(4):823-832.[doi:10.11992/tis.202110022]
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Improved barnacles mating optimizer to solve high-dimensional continuous optimization problems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 823-832 Column: 学术论文—人工智能基础 Public date: 2023-07-15
Title:
Improved barnacles mating optimizer to solve high-dimensional continuous optimization problems
Author(s):
ZHAO Shijie1; 2;  ZHANG Tianran1;  MA Shilin1;  WANG Mengchen1
1. Institute of Intelligence Science and Optimization, Liaoning Technical University, Fuxin 123000, China;
2. Institute for Optimization and Decision Analytics, Liaoning Technical University, Fuxin 123000, China
Keywords:
intelligence optimization algorithm; barnacles mating optimizer; sedimentation adhesion behavior; forward-and-backward decreasing casting strategy; local extremum avoidance; high dimensional optimization of function; global optimization; convergence precision
CLC:
TP391; TP301.6
DOI:
10.11992/tis.202110022
Abstract:
To strengthen the global exploration performance and local optimization accuracy of barnacles mating optimizer (BMO), an improved BMO (IBMO) is proposed based on the sedimentation adhesion behavior (SAB) of barnacle larva and the forward-and-backward decreasing casting (FBDC) strategy, which is applied to solve high-dimensional continuous optimization problems. Inspired by the behavior of barnacle larva floating with tide and spiraling sedimentation in nature, the SAB model is built to increase the population diversity and improve the global exploration capacity. Meanwhile, in accordance with reverse learning, and by integrating into the decreasing control mechanism, FBDC modifies the sperm casting process of traditional BMO to amplify the local search domain and improve the local exploitation ability. Experimental results verify that these two strategies can effectively improve the global exploration and local optimization exploitation performance of BMO. Compared with other recent intelligence algorithms, the proposed IBMO shows higher convergence accuracy, stronger robustness and good high-dimensionality applicability.
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