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。
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.
在连续潮流法的基础上，结合经过改进的差异进化算法，通过对变量的组合优化，求取系统静态电压稳定安全裕度。
By combining the continuous power flow method with improved differential evolution calculation, the static voltage stability margin is found via optimal grouping of relevant variables.
根据免疫学研究中抗原与抗体同时进化的特性，通过对动目标预测系统特点的分析，提出一种改进的免疫算法。
Based on the property that antigen and antibody are evolving simultaneously in immunology, an improved immune algorithm is presented through analysis of moving object forecast system.
通过与非进化模式下的多机器人地图构建方法的比较，该算法可以提高地图搜索的效率，加快全局地图的收敛。
And through compare with non-Evolutionary Map building method, the Evolutionary Reinforcement learning algorithm can increase search map efficiency and expedite convergence speed of global map.
通过对不同情况算例的仿真，验证了神经网络目标预测算法和基于抗原进化免疫算法的正确性和有效性。
Correctness and validity of the neural network object forecast algorithm and immune algorithm with evaluated antigen are tested by the simulation of varies examples.
遗传算法是一种借鉴生物界自然选择和自然进化机制的搜索方法，通过对个体进行复制、交叉、变异操作完成搜索过程。
Genetic algorithms (GAs) are search algorithms based on of natural evolution processing including selection, mutation and crossover operations on the genes of individuals or potential solutions.
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.
通过在遗传算法中引入精英保留的进化策略，保证算法的收敛性，改善优化结果。
The elitist strategy was introduced into GA to improve the convergence and optimization results.
通过直接将量子位的Bloch坐标视为基因位，提出一种基于量子位Bloch坐标的量子衍生进化算法。
By directly regarding the Bloch coordinates of qubit as genes in chromosome, a quantum-inspired evolution algorithm is proposed.
通过直接将量子位的Bloch坐标视为基因位，提出一种基于量子位Bloch坐标的量子衍生进化算法。
By directly regarding the Bloch coordinates of qubit as genes in chromosome, a quantum-inspired evolution algorithm is proposed.
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