A collaborative competition multitasking framework for constrained multi-objective optimization
Xinyu Feng, Qianlong Dang, Xiaochuan Gao, Yanghui Wu, Lifei Zheng · Applied Soft Computing · 2025
Constrained multi-objective optimization problems (CMOPs) are common in the real world. Constrained multi-objective evolutionary algorithms (CMOEAs) based on evolutionary multi-tasking show excellent performance in solving CMOPs. However, not all tasks can find useful information during the process of evolution, which inevitably results in a waste of computing resources. In this paper, a CMOEA based on collaborative competition multitasking (TCCMT) is proposed, in which two auxiliary tasks are constructed to co-evolve with the main task in a collaborative competition manner. During the process of evolution, only the dominant auxiliary task is selected to help the main task evolve, which reduces the resource consumption to evolve the invalid tasks. Meanwhile, the evolutionary process is divided into three stages in order to balance exploration and exploitation. The auxiliary tasks customize the constrained adaptive regression strategy and double angle enhancement strategy respectively to improve the ability to solve different problems. Compared with the nine most advanced CMOEAs on 33 benchmark problems and 7 real-world engineering problems, the Friedman test results show that TCCMT achieves the best rank on all test problems and exhibits a statistically significant difference.