Papers
4
Total Citations
457
H-Index
4
About
Xinye Cai is a leading researcher in evolutionary computation, with a primary focus on constrained multi-objective optimization problems (CMOPs). His most influential contribution is the development of an improved epsilon constraint-handling method integrated with the decomposition-based multi-objective evolutionary algorithm (MOEA/D), which effectively addresses CMOPs featuring large infeasible regions. This work, published in 2019, has garnered over 415 citations, underscoring its significant impact on the field. Cai’s research extends to practical applications, including the multi-objective optimization of teaching manipulators, and he has also explored innovative approaches in image security, such as using 1D-chaotic maps for real-time image-based systems. His work bridges theoretical advances in optimization algorithms with real-world engineering challenges, making him a key figure in the development of efficient and robust multi-objective optimization techniques.
Research Focus
Key Achievements
Top Papers
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- 3Analysis and multi-objective optimization of a kind of teaching manipulator10 citations · 2019
- 4