Correlation-Strength-Driven Self-Adaptive Strategy Adjustment Algorithm for Constrained Optimization

Yinghan Hong, Sirui Liang, Guizhen Mai, Yueting Xu, Han Huang, Jiahao Lian, Wei He, Dan Xiang, YuLin Li and Pinghua Chen
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Yinghan Hong: Guangzhou Maritime University, China
Sirui Liang: Hanshan Normal University, China
Guizhen Mai: Guangzhou Maritime University, China
Yueting Xu: South China Agricultural University, China
Han Huang: South China University of Technology, China
Jiahao Lian: Guangdong University of Technology, China
Wei He: Guangzhou Maritime University, China
Dan Xiang: Guangzhou Maritime University, China
YuLin Li: Guangdong University of Technology, China
Pinghua Chen: Guangdong University of Technology, China

International Journal of Swarm Intelligence Research (IJSIR), 2025, vol. 16, issue 1, 1-29

Abstract: Constrained optimization problems involve the simultaneous optimization of objectives and satisfaction of complex constraints, presenting a significant challenge for their solution using evolutionary algorithms (EAs). Compared with traditional EAs using dynamic allocation mechanisms, the authors propose the correlation-strength-driven self-adaptive-strategy adjustment (CSA) algorithm. It quantifies dynamic objective-constraint correlations into a strength coefficient to select constraint or objective priority criteria as initial optimization and adjusts priorities in real time based on feasible solution ratios and optimal objective changes, enabling intelligent strategy switching without manual intervention. The study shows that dynamically balancing priorities between objective improvement and constraint violation reduction enhances allocation efficiency and solution quality. Experiments on CEC2006, CEC2010, and CEC2017 datasets confirm CSA's faster convergence and higher-quality solutions.

Date: 2025
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