We have designed and realized cutting-edge metaheuristic local search techniques for solving complicated combinatorial optimization problems.
Some are embedded in Cooperative Group Optimization Systems, for solving Travelling Salesman Problem (TSP), Graph Coloring Problem (GCP), Quadratic Knapsack Problem (QKP), Flow-Shop Scheduling Problem (FSP), and Quadratic Assignment Problem (QAP), etc. Some are of stand-alone local search forms for solving the Magic Square Problem, finding narrow admissible k-tuples, and tacking Timetabling Problems, etc.
- X.-F. Xie, Z.-J. Wang. Exploiting problem structure in combinatorial landscapes: A case study on pure mathematics application. International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA, 2016.
- X.-F. Xie. Round-table group optimization for sequencing problems. International Journal of Applied Metaheuristic Computing, 2012, 3(4): 1-24. [DOI]
- X.-F. Xie, J. Liu. Multiagent optimization system for solving the traveling salesman problem (TSP). IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39(2): 489-502. [DOI]
- X.-F. Xie, J. Liu. Graph coloring by multiagent fusion search. Journal of Combinatorial Optimization, 2009, 18(2): 99-123. [DOI]
- X.-F. Xie, J. Liu. A mini-swarm for the quadratic knapsack problem. IEEE Swarm Intelligence Symposium (SIS), Honolulu, HI, USA, 2007: 190-197. [DOI]
- X.-F. Xie, J. Liu. How autonomy oriented computing (AOC) tackles a computationally hard optimization problem. International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Hakodate, Japan, 2006: 646-653. [DOI]