针对这些问题，对基本遗传算法引入了邻域操作、自适应策略和混沌优化等多种改进策略，研究设计了一种有机结合各种改进策略的改进遗传算法流程。
To avoid these problems, some methods including adjacent-domain operations, adaptability and chaos have been taken into consideration in this paper to improve the capability of the algorithm.
混沌和遗传算法的结合产生了混沌遗传算法。
Chaos genetic algorithms are the combination of chaos and genetic algorithms.
在满足设计要求和实际工况的条件下，通过混沌遗传算法和虚拟样机技术对该机构进行了优化设计，降低了成本，提高了生活垃圾装箱效率。
On the condition of design requirement and actual working condition, optimize the design of this mechanism by CGA and Virtual Prototype Technology, cut down the cost and improve loading efficiency.
遗传算法和混沌遗传算法的收敛速度比经典的线性优化方法慢很多，但改进后其速度还是可以接受的，且混沌遗传算法的收敛速度比同等条件下的遗传算法速度快得多。
Both the convergence speeds of CGA and GA are much slower than the classic linear methods, but their speeds after improved are acceptable, and the convergence speed of CGA is faster than that of GA.
对于模型的求解方法，构造了一种自适应的混沌遗传算法，采用自然数编码方式，动态的在线调整算法的交叉和变异概率，并采用混沌优化方法作为变异算子。
The algorithm used natural number coding method with dynamically adjustment for the probability coefficients of crossover and mutation, and used chaos optimization method as the mut.
算例结果表明，混沌优化方法比常规的数值算法和遗传算法更为优越。
Calculation results showed that chaotic optimization method was superior to conventional methods and genetic arithmetics.
实验结果表明，与遗传算法相比，混沌遗传算法用于阈值寻优减少了运算时间，提高了收敛率。
Simulation results show that using CGA to find optimal threshold can save more operation time than GA and improve convergence probability.
实验结果表明，与遗传算法相比，混沌遗传算法用于阈值寻优减少了运算时间，提高了收敛率。
Simulation results show that using CGA to find optimal threshold can save more operation time than GA and improve convergence probability.
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