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Yuxin Liu
Researcher at Southwest University
Publications - 24
Citations - 382
Yuxin Liu is an academic researcher from Southwest University. The author has contributed to research in topics: Travelling salesman problem & Ant colony optimization algorithms. The author has an hindex of 9, co-authored 23 publications receiving 303 citations. Previous affiliations of Yuxin Liu include Victoria University of Wellington & Shanghai Maritime University.
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Journal ArticleDOI
Solving NP-Hard Problems with Physarum-Based Ant Colony System
TL;DR: A Physarum-based pheromone matrix optimization strategy in ant colony system (ACS) for solving NP-hard problems such as traveling salesman problem (TSP) and 0/1 knapsack problem (0/1 KP) and results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs.
Proceedings ArticleDOI
Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem
TL;DR: A new Genetic Programming-based Hyper-Heuristic (GPHH) for automated heuristic design for UCARP is developed and it is found that eliminating the infeasible and distant tasks in advance can reduce much noise and improve the efficacy of the evolved heuristics.
Journal ArticleDOI
A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model.
TL;DR: Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives.
Journal ArticleDOI
A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem
TL;DR: The proposed algorithm, called Solution-Policy Coevolver, significantly outperforms the state-of-the-art algorithms to the uncertain capacitated arc routing problem for the ugdb and uval benchmark instances and it is discovered that route failure is not always detrimental.
Journal ArticleDOI
A New Evolutionary Multiobjective Model for Traveling Salesman Problem
TL;DR: An improved method for GAs based on a novel evolutionary computational model, named the Physarum-inspired computational model (PCM), which is based on the prior knowledge of the PCM and optimized to enhance the distribution of solutions.