Evolutionary computation is an effective method to solve optimization, search and learning problems, inspired by genetics and nature evolution.
进化计算是基于遗传学和自然演化思想的一个解决优化、搜索和学习问题的有效方法。
Ant Colony optimization (ACO) is a new-style simulating evolution algorithm. The behavior of real ant colonies foraging for food is simulated and used for solving optimization problems.
蚁群算法是一种新型的模拟进化算法，它通过模拟蚁群在觅食过程中寻找最短路径的方法来求解优化问题。
A mix of Differential Evolution and Particle Swarm Optimization can make a new algorithm.
差分算法与粒子群算法结合可形成新的算法。
Simulated evolution and simulated annealing are two stochastic search algorithms for solving the global optimization problems. They have been widely used in different engineering areas.
模拟进化和模拟退火是解决全局优化问题的随机搜索技术，它们在工程领域有着广泛的应用。
Immune algorithm is a completely new method band together with the principle of life-form evolution, optimization technique and computer technique.
免疫算法是一种生物进化原理、最优化技术和计算机技术相结合的全新算法。
Colony evolution based intelligent optimization algorithms have deficiencies of poor computational efficiency and are prone to premature during the solution process.
基于群体进化的智能优化算法在求解过程中存在计算效率低和易于早熟收敛等缺点。
A co evolution algorithm used to solve topology and size optimization of trusses is proposed.
针对桁架拓扑优化问题提出桁架拓扑和尺寸优化的协同演化算法。
The separation among populations and the adaptive-gathering in a population are achieved by local evolution, so the multi-model function optimization is transformed to unimodal function optimization.
以局部演化的方式，实现了种群间分离与种群内自聚集，使多峰函数优化问题转化为单峰函数优化问题。
The optimization problem was solved by differential evolution and the constraints were handled by feasibility-based rule.
利用差分进化算法求解该优化问题，并利用可行性规则处理约束。
During the evolution process, the used finite element participated the evolution and the shape optimization of supporting structure.
在整个演化程序的进行中，所用有限单元既可以参与演化，又可以参与支撑结构的形状优化。
Then the methods of fuzzy optimization and evolution programming are adopted to study the portfolio investment under a new risk concept, and an algorithm for solving the problem is given.
运用模糊优化和进化规划方法，研究新风险概念下的模糊证券组合选择，并给出了其相应算法。
We know that evolution can go beyond optimization and create new things to optimize.
我们发现进化体制能超越优选法并创造新事物以优化。
However, the structure adjustment and system optimization should be based on the integral understanding of the agricultural production system, and its regularity of systematic structural evolution.
农业结构的调整，必须建立在对系统整体认识的基础上，必须符合系统结构本身的演变规律。
And "model evolution" is proposed, namely, the forwards and backwards optimization can evolve the model.
提出通过正向优化和反向优化实现企业模型的演进。
The differential evolution is a simple, reliable and efficient global optimization algorithm.
差异进化算法是一种简单、可靠和有效的全局优化算法。
By analyzing the discretized density evolution theory and the differential evolution algorithm, a method is proposed to search optimal irregular LDPC code for joint optimization of the system.
在分析和阐述了离散概率密度演化理论和多维线性空间极值问题的基础上，给出了适合于该方案的非正则ldpc码的搜索算法和搜索结果。
It has been used in the optimization design of reticulated structures, but its defects make the evolution progress too long and too slowly.
为了加快遗传算法的进化过程，提出了基于遗传算法和满应力准则进行网格结构优化的杂交算法。
HSUPA is the optimization and evolution for packet service in uplink direction. It is another great evolution of TD-SCDMA standard after HSDPA.
TD - HSUPA是上行链路方向针对分组业务的优化和演进，它是继TD - SCDMAHSDPA后，TD - SCDMA标准的又一次重要的演进。
Differential evolution algorithm is applied to optimization problem of mechanical design and the basal theory and steps are analysed.
将差异演化算法应用于机械优化设计问题，具体分析了差异演化算法的基本原理和步骤。
Genetic algorithms (GA) are optimization and machine learning algorithms inspired by processes of natural evolution.
遗传算法是由受生物进化过程启发而形成的进行优化和机器学习的算法。
Propagation algorithm is a heuristic based on the principle of biological evolution parallel search and optimization techniques, often used for optimization.
传算法是一种基于生物进化原理的启发式并行搜索和优化技术，常被用于优化计算。
The method solves the problems which caused by dynamic planning dealing with multiple constraints and large optimization problems, and improves the precision of evolution algorithm.
计算表明，该方法避免了动态规划等算法处理多约束、大型优化问题的困难，同时提高了进化算法的精度。
The reactive power compensation ranges are obtained by the physical constraints of the distribution network, and thus the initialization and evolution are restricted to a reduced optimization space.
利用配电网络物理规则求得投切点无功补偿容量调节范围，用于约束解的产生和进化，缩小寻优空间。
The particle swarm optimization speeded up the evolution process, and improved the convergence speed and accuracy.
通过这种处理使得粒子群体的进化速度加快，从而提高了算法的收敛速度和精度。
The particle swarm optimization speeded up the evolution process, and improved the convergence speed and accuracy.
通过这种处理使得粒子群体的进化速度加快，从而提高了算法的收敛速度和精度。
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