混沌优化算法在非线性约束规划问题中的应用

综述了混沌优化算法在国内的研究历史与现状 ,介绍了算法的基本步骤 .针对非线性约束问题 ,提出了一种新的、可行的混沌优化算法 .实例计算表明 ,该算法稳定性好、简单、易于掌握 ,对于多维、非线性、复杂约束问题的求解具有普适性     

A hybrid genetic algorithm based on mutative scale chaos optimization strategy

In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms,a new hybrid gentic algorithm is proposed.In this algorithm,a mutative scale chaos optimization strategy is operated on the population after a genetic operation.And according to the searching process.The searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population.The algorithm has such advantages as fast search,precise results and convenient using etc.The simulation results show that the performance of the method is better than that of simple genetic algorithms.     

复杂函数优化的混沌遗传算法

将混沌融入遗传算法提出了混沌遗传算法,该方法利用混沌运动的随机性、遍历性、对初始条件的敏感性等特性进行群体的混沌初始化和最优个体的混沌变尺度载波寻优,典型复杂函数优化的仿真结果表明,该方法较遗传算法具有更快的收敛速度和更小的计算,是复杂函数优化的有效手段。     

混沌遗传算法研究及其在经济负荷分配问题中的应用

提出了一种实数编码混沌遗传算法,并将其用于经济负荷分配问题。该算法将混沌引入遗传算法,利用混沌对标准遗传算法中的选择和变异进行了改进,之后对每一代最优个体进行变尺度混沌优化。将该方法应用于某厂3机组经济负荷分配问题,通过与混沌优化方法以及传统遗传算法的比较,该方法可以求得高质量的可行解,表明了该方法在求解经济负荷分配问题的有效性。     

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.     

适用于复杂函数优化的多群体遗传算法

提出了一种适合于复杂函数寻优的多群体遗传算法．该方法对搜索区域进行划分，使每个子区域具有简单的函数形态．对每个子区域安排一个子群体进行搜索，这个过程可并行进行．仿真表明该方法速度快，可同时获得多个局部极值点．     

Tent混沌和模拟退火改进的飞蛾扑火优化算法

针对标准飞蛾扑火优化算法存在的易陷入局部最优陷阱、全局寻优能力不足的问题,借鉴混沌序列、模拟退火算法和遗传算法,提出Tent混沌和模拟退火改进的飞蛾扑火优化算法.首先,通过Tent混沌序列初始化种群,增加种群多样性;然后对当前最优解增加扰动产生新解,并与当前最优解按比例杂交相加,根据模拟退火算法中的Metropolis准则判断是否接受杂交后的新解,最终获得最优解.分别使用复杂高维基准函数和航迹规划问题测试算法性能.其中,6个复杂基准函数寻优测试结果表明,对于10维基准函数,该算法经过约0.25秒收敛到最优值;对于50维基准函数,该算法经过约0.5秒收敛到最优值.与标准飞蛾扑火优化算法和其它智能优化算法相比,该算法能够有效跳出局部最优解,寻优精度更高,收敛速度更快.航迹规划仿真表明,对有4个禁飞区和2个威胁源的空域环境,该算法经过大约100次迭代可以得到最优航迹,与标准飞蛾扑火优化算法相比精度更高,具有实际应用价值.因此,该算法具有更好的寻优性能.     

基于遗传算法和蚂蚁算法求解函数优化问题

针对遗传算法求解精度低以及蚂蚁算法求解速度慢的问题，提出一种基于遗传算法和蚂蚁算法的混合算法.该混合算法利用了遗传算法快速随机的全局搜索能力的优点，设计了编码与适应度函数，进行了种群生成与染色体的选择，并通过设定交叉算子和变异算子， 生成了信息素分布.该混合算法利用了蚂蚁算法正反馈以及具有分布式并行全局搜索能力的优点，通过确定吸引强度的初始值，建立了强度更新的模型，从而求得精确解.并将该算法应用于求解函数优化问题.结果表明，该混合算法与遗传算法和蚂蚁算法相比，收敛速度快，寻优性能好.     

Multi-objective optimization design of airfoil and wing

To extend available monoobjective optimization methods to multiobjective and multidisciplinary optimization, the construction of a suitable resultant objective function(in deterministic method-DM) or a fitness function(in genetic algorithm-GA) is important. An objective function combination method (OFCM) of constructing such a function for constrained optimization problems is suggested. How to use both deterministic and genetic algorithms to biobjective and bidisciplinary optimal design of high performance airfoils and wings is discussed. Numerical results in both 2D (airfoil) and 3D (wing) cases show that the present method can be used to optimaize different kinds of initial airfoils and wings. The performance of optimized shape is improved significantly. The method is successful and effective.     

基于改进遗传算法的无功优化

遗传算法的基础上对其局限性进行改进,使该算法在电力系统无功优化的应用中具有一定优越性。通过改进编码和选择算子,自适应的交叉变异概率等策略,并引入基于模拟退火策略的适应度函数和混沌算法,使得改进遗传算法高速、准确的收敛于最优解,改善了传统遗传算法易陷入收敛性差、效率低的弊端。在此基础上建立无功优化数学模型,介绍了该算法具体实现步骤,并将其应用于IEEE30节点,证明所提算法是可行和有效的。     

Genetic algorithms in bidisciplinary (aerodynamics/electro-magnetism ) optimization

The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced . Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.     

变尺度混沌优化方法在电站经济运行中的应用

变尺度混沌优化方法（Ｍｕｔａｔｉｖ Ｓｃａｌｅ Ｃｈａｏｓ Ｏｐｔｉｍｉｚａｔｉｏｎ Ａｌｇｏｒｉｔｈｅｍ,ＭＳＣＯＡ）利用混沌现象的特点,把混沌变量映射到待寻优变量区间,不断缩小优化变量的搜索空间和提高搜索精度,从而有较高的搜索效率。对几个测试函数的优化计算验证了该方法有的效性。电站经济运行问题是一个具有整型变量、连续变量及非线性函数的混合整数非线性规划,本语文将变尺度混沌优化方法首次应用到电站     

一种基于混合优化算法的阵列波束形成方法

提出一种波束图设计的数值方法.该方法是组合运用了自适应阵原理和遗传算法的混合二阶优化算法.方法第一步是基于自适应理论,假定阵列为一自适应阵,通过在其波束图的旁瓣区布置大量的干扰信号以实现旁瓣控制.通过多次调整干扰强度,可以获得初始波束图.根据初始波束图建立新的期望波束图,基于估计波束图和期望波束图之间差别,建立误差能量函数.采用标准遗传算法通过最小化误差能量函数来实现波束图优化设计.双圈圆环阵的设计实例说明了算法的有效性.设计波束图的具体参数表明了其优良的性能.对于任一给定阵,该方法可设计给定指向方位、具有更低旁瓣的波束图.如果不能设计出期望特性的波束图,该方法可设计出最佳可得的波束图.     

遗传算法在同轴式三级减速器结构优化设计中的应用

针对同轴式三级减速器的结构设计要求,提出了一种基于遗传算法的优化设计方法.建立数学模型,并采用遗传算法进行优化,容易得到全局最优解.通过同轴式三级减速器结构的优化设计结果表明,优化效果显著.     

改进的遗传算法在结构优化设计中的应用

遗传算法(GA)是基于达尔文进化论和遗传学说形成的一种崭新的优化算法.它具有全局收敛性和并行性;对先验知识要求较少,具有很强的适应性.针对结构优化设计方法中存在的局限性,将改进的遗传算法用于结构优化设计中.改进的GA采用以下措施提高搜索效率:(1)动态调整变量区间和GA参数;(2)在每一轮进化结束后重新初始化群体,开始新的进化;(3)将最优个体保留到下一轮.据此编制了计算机程序,并将其应用到一个桁架结构的优化实例中.运行结果表明,改进后的遗传算法用于结构优化设计能够有效地避免陷入局部最优解的现象,提高了搜索效率,具有较强的适应性.     

Multidisciplinary design optimization for air-condition production system based on multi-agent technique

In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.     

基于惯性因子的混沌粒子群优化算法研究

粒子群优化算法本身在多峰复杂函数时会出现早熟收敛现象,降低粒子的多样性,导致粒子群不能收敛到全局极值点。针对粒子群优化算法的局限性,把混沌优化思想引入到粒子群算法,采用混沌优化粒子群算法对测试函数进行仿真,并在此基础上加入惯性因子对混沌优化粒子群算法进一步改进,Matlab仿真结果表明,改进的混沌优化粒子群算法,结合了混沌和粒子群算法共同的优点,能快速、准确地搜索到全局最优值。     

钢框架优化遗传算法的若干改进

将遗传算法应用于钢框架结构优化设计时，在实际的应用中，基本的遗传算法存在着收敛速度慢和稳定性差等缺陷。为克服这些问题，采用了改进的适应度函数和约束处理方法、自适应的交叉、变异概率和最优保存策略，提高了遗传算法的效率和可靠性。算例表明改进的遗传算法对离散变量结构优化是有效、可行的。     

改进遗传算法在桁架拓扑优化中的应用

基于桁架拓扑优化,对遗传算法提出了一些改进措施,形成了一种高效综合的遗传算法。在桁架的截面尺寸和拓扑结构混合设计中,对尺寸变量和拓扑变量分别进行二进制编码、交叉和变异,得到桁架拓扑结构和杆件截面尺寸的初解,适当降低尺寸变量编码精度,以加快算法的收敛速度。然后对截面尺寸重新编码,以较高的尺寸精度进行搜索,为了防止陷入局部最优解,取部分初解加入新的父代。算例表明,该算法对离散变量的桁架拓扑优化是快速有效的。     

基于混沌搜索的混和粒子群优化算法

所提出的算法将粒子群优化算法和混沌算法相结合，既摆脱了算法搜索后期易陷入局部极值点的缺点，同时又保持了前期搜索的快速性，最后通过4个测试函数将该算法与基本粒子群算法进行仿真对比，比较结果表明基于混沌搜索的混和粒子群优化算法在收敛性和稳定性等方面明显优于基本粒子群优化算法．