基于遗传算法的有限推力交会轨道优化仿真

针对有限推力空间飞行器交会对接逼近段燃料/时间组合最优轨道优化问题,提出了一种采用遗传算法求解的优化策略.以C-W方程为交会模型,将整个逼近段分为若干弧段,在每个弧段内追踪航天器均采用常值推力机动,这样可以求得C-W方程在每个弧段内的解析解,于是,交会对接逼近段的轨道优化问题可以转化为具有非线性约束的数学规划问题,最后采用广义拉格朗日-遗传算法对该问题进行了优化求解.数学仿真结果表明,该方法可以很好的解决交会对接终端逼近段燃料/时间组合最优轨道优化问题.     

一种改进的自适应遗传算法

遗传算法作为一种模仿生物自然进化过程的随机优化算法,对求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。传统的自适应遗传算法虽能有效提高算法的收敛速度,却难以增强算法的鲁棒性。该文提出了一种改进的自适应遗传算法,对交叉率和变异率进行了优化,实现了交叉率和变异率的非线性自适应调整。实验结果表明,相比传统的自适应遗传算法,新算法具有更快的收敛速度和更可靠的稳定性。     

自适应遗传算法交叉变异算子的改进

标准遗传算法采用固定的交叉率和变异率,对于求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。传统的自适应遗传算法虽能有效提高算法的收敛速度,却难以提高优良解的多样性,算法的鲁棒性仍有待改善。文章提出了一种改进的自适应遗传算法,对交叉算子和变异算子进行了优化,实现了交叉率和变异率的非线性自适应调整。实验结果表明,相比传统的自适应遗传算法,新算法具有更快的收敛速度和更可靠的稳定性。     

舰船补给物料搬运顺序优化问题的改进遗传算法

现有的多搬运工具可并行条件下的物料搬运顺序优化模型, 其采用的标准遗传算法收敛速度慢且易陷入局部最优. 提出了该模型的改进遗传算法, 采用精英保留策略代替传统的轮盘选择方法, 使用自适应策略设计交叉算子和变异算子. 以某一具体的舰船补给物料搬运顺序优化问题为背景, 通过实例进行了计算. 结果表明, 改进遗传算法收敛速度大大提高, 具有较高的求解质量和效率.     

改进的自适应遗传算法在函数优化中的应用*

针对传统遗传算法在复杂函数优化的寻优搜索中容易陷入局部极值,搜索效率低,不稳定等特点,提出一种改进的自适应遗传算法,该算法的思想是根据进化中种群适应度的集中分散的程度非线性地自适应调节遗传进化的运算流程和交叉概率Pc、变异概率Pm的值,从而能更好地产生新的个体摆脱局部极值搜索到全局最优解,并采取最优保存策略来保证改进的自适应遗传算法的收敛性。仿真实验结果表明,与现存其他算法相比,改进的自适应遗传算法在全局寻优的收敛速度、最优解、求解精度、和稳定性等方面都有了较大的改进和提高。     

基于改进的遗传算法在函数优化中的应用

针对传统遗传算法在函数优化过程中容易陷入局部最优解、收敛慢等缺点,提出了一种新的自适应遗传算法NAGA。该算法考虑了种群适应度的多种集中分散程度,并且非线性地自适应调节遗传算法的交叉概率与变异概率;为了加快寻优效率,在选择算子方面将引进的选择算子与最优保存策略相结合;为了使遗传操作过程中种群数量恒定,又提出了保留亲本的策略。通过仿真实验发现,与经典遗传算法GA和IAGA相比,改进的自适应遗传算法在收敛速度与精准度等方面都有较大的进步。     

一种多策略结合的改进文化算法

为了更有效地抑制文化遗传算法的早熟收敛现象和提高收敛速度,提出了一种多策略结合的文化遗传算法。该算法在信念空间,使用与文化算法不同的接受函数、影响函数和更新函数,在群体空间,针对种群采取多种群化,并采用自适应的交叉变异操作且多种群之间加入竞争机制的遗传算法,这样使得改进后的算法具有更强的全局寻优能力和局部寻优能力,有效避免陷入局部最优,抑制了早熟收敛,提高了收敛效率。用上述算法对几个典型函数进行优化,实验证明了多种群自适应的文化遗传算法的有效性和可行性,新的算法不易陷入早熟收敛,此外全局搜索能力和局部搜索能力得到有效平衡,收敛率高。     

基于空间交配遗传算法的收敛性分析

基于空间交配遗传算法(GASM)采用空间交配遗传算子,有效克服早熟收敛问题,但缺少相关理论分析。文中采用马尔可夫链分析基于空间交配遗传算法的收敛性。证明采用最优个体保留机制的GASM,可收敛到全局最优解。同时证明在没有变异算子的情况下,GASM以概率1收敛到全局最优解。通过4个测试问题(其中3个为多峰值复杂问题)的对比实验,结果表明,GASM在求解多峰值复杂问题时,比采用最优个体保留机制的经典遗传算法,具有更好的收敛性。同时也与快速蜂群优化算法进行比较实验。     

基于混合算法的航天器轨道规划方法

空间在轨服务过程中,当目标航天器周围有若干小卫星环绕时,服务航天器要避开小卫星的安全范围,与目标航天器成功交会并进行在轨服务,航天器的机动轨道规划是其重要前提;在路径规划中,遗传算法应用广泛,但是求解实际问题的时间容易受到染色体基因等算子数目的影响,求解效率未得到保证;提出了一种混合遗传算法,将遗传算法全局搜索能力和模拟退火算法较强的局部搜索能力进行整合,以服务航天器机动轨道的路径安全、任务时间、燃料消耗、总路程等为约束条件,并对算子进行特殊设计,规划出最优机动轨道路径;通过场景假设和仿真实验证明,该混合遗传算法能够规划出符合约束条件的最优机动轨道路径,并且极大地提高了求解效率。     

基于混合算法的环形轨道RGV系统调度优化研究

针对自动化仓库中环形轨道RGV（有轨制导车辆）调度问题,以任务最短完成时间为目标,分析其主要影响因素。在此基础上提出路径最短和堵塞次数最少两个优化目标,并建立数学模型,设计基于规则的遗传算法求解。使用自适应的交叉变异概率代替传统遗传算法中的固定参数,改善遗传算法易陷入局部最优解的现象。同时,为解决多目标优化求解问题,提出了改进的自适应权重的求解方案。通过Matlab仿真实验分析比较算法性能,验证了算法的有效性。     

遗传算法用于结晶过程动力学参数辩识

遗传算法是一类随机优化方法。常被用于解决复杂的优化问题,基于群体的搜索,重组和变异是遗传算法区别于其他优化方法的主要特征。文章中将遗传算法应用于过饱和溶液Ｌｉ２Ｏ．３Ｂ２Ｏ３－Ｈ２Ｏ体系结晶过程动力学参数辨识,确定了结晶反应速率常数、热力学平衡浓度和表观反应级数。     

Optimal genetic manipulations in batch bioreactor control

Advances in metabolic engineering have enabled bioprocess optimization at the genetic level. Large-scale systematic models are now available at a genome level for many biological processes. There is, thus, a motivation to develop advanced control algorithms, using these complex models, to identify optimal performance strategies both at the genetic and bioreactor level. In the present paper, the bilevel optimization framework previously developed by the authors is coupled with control algorithms to determine the genetic manipulation strategies in practical bioprocess applications. The bilevel optimization includes a linear programming problem in the inner level and a nonlinear optimization problem in the outer level. Both gradient-based and stochastic methods are used to solve the nonlinear optimization problem. Ethanol production in an anaerobic batch fermentation of Escherichia coli is considered in case studies that demonstrate optimization of ethanol production, batch time, and multi-batch scheduling.     

An impulse control approach to spacecraft autonomous rendezvous based on genetic algorithms

This paper proposes a novel approach to spacecraft impulse autonomous rendezvous by using genetic algorithms. Based on the Clohessy-Wiltshire (C-W) equations, the whole rendezvous process is described as a switching system composed of closed-loop system and open-loop system, which correspond to the impulse action phase and free motion phase during the rendezvous process. Based on Lyapunov theory, the autonomous rendezvous problem is regarded as an asymptotic stabilization problem. By introducing two virtual energy functions, the stability of the switching system is analyzed, and the duration of the impulse action and the thrust limitation are considered synthetically. Then, a state-feedback controller design method is proposed, and an approach based on linear matrix inequality and genetic algorithm (GA) is proposed to solve the controller design problem and the calculation steps are presented. With the designed controller, the impulse thrust which satisfies the given thrust constraint is determined according to the real-time relative state between two spacecraft at the impulse instant, and the impulse duration is kept as short as possible. The effectiveness of the proposed approach is illustrated by simulation examples.     

基于惩罚和修复混合策略的约束处理方法

约束处理是约束优化的关键问题，特别是非线性约束的处理一直缺少特别有效的解决方法，将惩罚函数法与修复策略结合使用，可以有效地避免迭代过程中大量非可行解的产生，使得约束优化问题在惩罚函数和修复算子的协同作用下收敛于全局最优，较好地解决了在遗传算法约束优化问题中单独使用惩罚和修复方法时一些难以解决的问题。基于随机方向法构造的修复算子作用效果显著，采用多个测试函数对算法进行检验，均能较好地收敛于可行域中的最优解，验证了算法的可靠性。     

Modified differential evolution approach for optimization of planar parallel manipulators force capabilities

The global optimization problem is not easy to solve and is still an open challenge for researchers since an analytical optimal solution is difficult to obtain even for relatively simple application problems. Conventional deterministic numerical algorithms tend to stop the search in local minimum nearest to the input starting point, mainly when the optimization problem presents nonlinear, non-convex and non-differential functions, multimodal and nonlinear. Nowadays, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. The primary advantage of EAs over other numerical methods is that they just require the objective function values, while properties such as differentiability and continuity are not necessary. In this context, the differential evolution (DE), a paradigm of the evolutionary computation, has been widely used for solving numerical global optimization problems in continuous search space. DE is a powerful population-based stochastic direct search method. DE simulates natural evolution combined with a mechanism to generate multiple search directions based on the distribution of solutions in the current population. Among DE advantages are its simple structure, ease of use, speed, and robustness, which allows its application on several continuous nonlinear optimization problems. However, the performance of DE greatly depends on its control parameters, such as crossover rate, mutation factor, and population size and it often suffers from being trapped in local optima. Conventionally, users have to determine the parameters for problem at hand empirically. Recently, several adaptive variants of DE have been proposed. In this paper, a modified differential evolution (MDE) approach using generation-varying control parameters (mutation factor and crossover rate) is proposed and evaluated. The proposed MDE presents an efficient strategy to improve the search performance in preventing of premature convergence to local minima. The efficiency and feasibility of the proposed MDE approach is demonstrated on a force optimization problem in Robotics, where the force capabilities of a planar 3-RRR parallel manipulator are evaluated considering actuation limits and different assembly modes. Furthermore, some comparison results of MDE approach with classical DE to the mentioned force optimization problem are presented and discussed.     

基于二重编码的遗传算法求解连接增强问题

连接增强问题是个组合优化问题,遗传算法适合解决组合优化问题,一般的遗传算法都采用一重编码方法,这里采取二重编码方法来解决连接增强问题,采取了自适应方法来调整交叉和变异概率,模拟实验中比较了二重编码遗传算法和一重编码的遗传算法的性能。     

基于二重结构编码的遗传算法求解连接增强问题

连接增强问题是个组合优化问题,遗传算法适合解决组合优化问题,一般的遗传算法都采用一重编码方法,本文采取二重编码方法来解决连接增强问题,采取了自适应方法来调整交叉和变异概率,模拟实验中比较了二重编码遗传算法和一重编码的遗传算法的性能。     

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or “good” initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data.     

Multiobjective firefly algorithm for continuous optimization

Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.     

改进遗传算法整定自抗扰控制器参数及应用

非线性自抗扰控制器策略(ADRC)耦合参数众多,单一机制优化算法在进行参数整定时易陷入局部最优解,极大地降低了自抗扰控制器的控制精度。针对此问题,该文提出了一种改进遗传算法(KFC-2PMGA)进行自抗扰控制器参数整定,将核模糊聚类算法应用到遗传算法双种群划分中,并针对聚类所得双种群,分别采用不同的自适应交叉及变异策略,有效地避免了传统遗传算法易产生"早熟"的现象。并以永磁同步电机为例进行仿真验证。结果表明,优化后的自抗扰控制器具有良好的系统响应及控制精度,改进后的遗传优化算法适用于复杂非线性自抗扰控制器参数整定。