组合优化多智能体进化算法

提出了一种新的组合优化方法——组合优化多智能体进化算法．该方法将智能体固定在网格上，而每个智能体为了增加自身能量将与其邻域展开竞争，同样智能体也可进行自学习来增加能量．理论分析证明算法具有全局收敛性．在实验中，作者分别用强联接、弱联接、重叠联接等各种类型的欺骗函数对算法的性能进行了全面的测试，并将算法用于解决具有树状等级结构的问题．比较结果表明文中算法所需的计算量远远小于其它方法，具有较快的收敛速度．为了测试算法解决大规模问题的能力，作者还将算法用于解决上千维的欺骗问题和等级问题，结果表明该文算法的计算复杂度与问题规模成多项式的关系．此外，将算法用于上千维的欺骗问题和等级问题，在国内外还均未见报到．

Combinatorial Optimization Using Multi-Agent Evolutionary Algorithm

In this paper, multi-agent systems and evolutionary algorithms are integrated to form a new algorithm, Multi-Agent Evolutionary Algorithm for Combinatorial Optimization (COMAEA). All agents live in a lattice like environment, with each agent fixed on a lattice-point. In order to increase energies, they compete with their neighbors, and they can also use knowledge. Theoretical analyses show that COMAEA converges to the global optimum. In the experiments, various deceptive problems, such as the ones with strong-linkage, weak-linkage and overlapping-linkage, and hierarchical problems are used to evaluate the performance of COMAEA. In order to test the performance of COMAEA on solving large-scale problems, COMAEA is used to solve deceptive problems and hierarchical problems with thousands of dimensions, and its computational complexity is analyzed. All experimental results show that COMAEA has a low computational cost and obtains good performance.