Z
Zixing Cai
Researcher at Central South University
Publications - 122
Citations - 5419
Zixing Cai is an academic researcher from Central South University. The author has contributed to research in topics: Mobile robot & Evolutionary computation. The author has an hindex of 25, co-authored 122 publications receiving 4699 citations. Previous affiliations of Zixing Cai include University of Nebraska Omaha.
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Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
TL;DR: A novel method, called composite DE (CoDE), has been proposed, which uses three trial vector generation strategies and three control parameter settings and randomly combines them to generate trial vectors.
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Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization
Hui Liu,Zixing Cai,Yong Wang +2 more
TL;DR: A novel hybrid algorithm named PSO-DE is proposed, which integrates particle swarm optimization (PSO) with differential evolution (DE) to solve constrained numerical and engineering optimization problems.
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A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
Zixing Cai,Yong Wang +1 more
TL;DR: The empirical evidence suggests that the new approach is robust, efficient, and generic when handling linear/nonlinear equality/inequality constraints and the best, mean, and worst objective function values and the standard deviations.
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An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
TL;DR: The empirical results suggest that the new adaptive tradeoff model (ATM) outperforms or performs similarly to other state-of-the-art techniques referred to in this paper in terms of the quality of the resulting solutions.
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Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems
Yong Wang,Zixing Cai +1 more
TL;DR: An improved version of the CW method, called CMODE, which combines multiobjective optimization with differential evolution to deal with constrained optimization problems is proposed, with the purpose of guiding the population toward promising solutions and the feasible region simultaneously.