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中文摘要：
旅行商问题已被证明是高维非线性完全问题,实际中还会增加非流通图约束.鉴于传统遗传算法在求解过程中出现早熟收敛、冗余迭代的缺陷,提出了混沌遗传算法.采用基于旅行商遍历城市顺序的染色体编码,结合随机法与贪心法以生成包含较优值的初始种群,避免出现大量非可行染色体,提高了后续的遗传效率.接着,执行优先保留交叉和平移变异操作,引入局部邻域及混沌搜索以加快算法收敛,还给出了最优解是否满足非连通约束的判据.最后,实验结果验证了该算法的有效性.
英文摘要：
Traveling salesman problem（TSP） was demonstrated to be non-deterministic polynomial complete problem,let alone unconnected graph constraint.Because the traditional genetic algorithm（TGA） had defects of premature convergence and slow convergence,a chaos genetic algorithm（CGA） was put forward.The CGA adopted the chromosome encoding scheme based on sequence of city which traveling salesman passed through,combined stochastic method and greedy method ways to produce the initial populations so as to contain optimal value,avoid infeasible chromosomes and improve the subsequently genetic efficiency.Then,precedence preservation crossover and shift change mutation operations were executed.At the same time,local neighborhood and chaos search were introduced to accelerate convergence.Furthermore,the criterion was given to judge whether optimal solution meets unconnected graph constraints or not.Finally,computation results proved the effectiveness of CGA.
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