精英克隆局部搜索的多目标动态环境经济调度差分进化算法

为有效解决复杂多目标动态环境经济调度问题,提出一种基于精英克隆局部搜索的多目标动态环境经济调度差分进化算法.以传统的差分进化(differential evolution,DE)算法为框架,为了提高DE算法的开采和探索能力,增设精英群的克隆和突变机制,采用动态选择方式确定精英群,有效增强算法的全局搜索能力.数值试验以I...     

基于多学习多目标鸽群优化的动态环境经济调度

针对电力系统动态环境经济调度(DEED)问题,提出了一种基于多学习策略的多目标鸽群优化(MLMPIO)算法.在多学习策略中,种群个体可以向外部存档集中的多个全局最优位置以及个体的历史最优位置进行学习,进而保持种群的多样性和全局搜索能力,避免陷入早熟收敛.引入了小概率变异扰动机制,进一步增强种群的多样性.为提升算法的运行效率,采用容量自适应变化的外部存档集来存储当前Pareto最优解集.为验证所提算法的性能,将MLMPIO应用于10机组电力系统的DEED问题求解.仿真结果证明了MLMPIO算法解决此类问题的可行性和有效性.     

基于综合集成与智能优化的复杂运输调度系统

探讨了一种解决复杂运输调度问题的新方案.首先从现实需求和案例分析出发,阐述了复杂运输调度问题具有的异构信息集成、多约束、模型动态时交、环境不确定等特征.在目前常用的几类相关方法(包括精确算法、传统启发式算法等)的基础上,指出了采用单一技术和方法在解决实际运输调度问题中的不足.提出了一种基于综合集成和智能优化的决策支持框架.强调人机结合的多层次定性定量分析方法,建立了一种层次化的决策模式和结构化的约束处理和求解机制.在集成多种使能技术的基础上,为供应链管理中的运输调度问题提供了新的求解方案.     

基于粒子滤波的动态进化算法

粒子滤波(PF)中粒子的选取与保留过程暗含着"优胜劣汰"的特点,因此PF和进化算法(EA)有着相近的仿生学特点,能够很"自然"进行结合。文章针对具有可学习性的非随机动态优化问题,提出一种基于粒子滤波的动态进化算法,使用PF在决策空间中对最优点的变化进行预测以启发进化算法的搜索。提出一种自适应种群多样性控制方法,用以协调EA和PF对算法的影响。使用移动峰(MPB)问题对算法和随机迁移算法(RIGA)进行了对比测试。实验结果表明所提出的算法是正确、有效的,能够更为有效地求解动态优化问题。     

关键设备工序紧凑的动态调度算法

针对求动态Job-Shop调度最优解这一复杂问题，提出了通过对不同时刻开始加工产品加工树的分解方法，将产品加工工序分为存在具有惟一紧前、紧后相关工序和独立工序，在对这两类工序分批、综合研究时，应用拟关键路径法(ACPM)和最佳适应调度方法(BFSM)调度，并考虑了关键设备的工序紧凑性、通过分析与实例验证，所提出的调度方法对解决动态的Job-Shop调度问题不仅算法简练，而又效果较好。     

基于云免疫克隆算法的空车动态优化问题

通过给定的时间轴将动态空车调度优化问题转化为一系列静态调度问题,以效益最大化为目标函数,考虑空车走行的时间对约束条件的影响,构建基于云偏好度的空车动态优化模型,并结合云模型对免疫克隆算法进行改进,提出一种云免疫克隆算法。算法根据应用偏好信息为抗体进行三维编码,通过计算抗体种群的熵进行免疫克隆操作,并利用云模型的分散稳定性对抗体免疫基因进行重组操作与变异操作,改善了向最优解的高效收敛能力。实验结果分析表明,该算法能改善空车动态调度系统的可用性、负载均衡离差、有效时间等方面的性能,满足了动态调度实时计算的实际需求。     

基于熵模型的动态粒子群优化算法

受多种群并行寻优机制的启发,提出了一种基于熵模型的动态粒子群优化算法(entropy dynamic multiPSO,EDM-PSO)用于处理动态优化问题.将解空间划分为多个子空间,在每个子空间中利用熵模型增加种群多样性,多种群并行搜索,利用多点环境检测机制检测环境变化.对动态多峰benchmark优化问题进行了数值实验,并与其他几种动态优化算法进行了比较,结果表明:EDM-PSO算法对于处理动态优化问题具有优势.     

特殊工艺约束下最小完工时间并行多机调度问题的研究

非等同并行机最小化完工时间调度问题作为家纺企业车间调度问题的重要组成部分,有着独特的特点,一方面并行多机非等同,另一方面每机器可生产产品类型受特殊工艺的约束,针对该问题的特点,基于免疫系统的克隆选择原理,结合一种新型的促进和激励群体多样性的技术,提出了一个新颖的人工免疫算法.仿真结果表明,此算法是有效的,优于遗传算法和克隆选择算法,并能适用于解实际家纺企业这类调度问题.     

基于均匀网络理论的配电网协调优化

针对含可调度式分布式电源的配电网优化问题,提出一种基于均匀网络理论的配电网协调优化方法。首先通过基尔霍夫电压和电流定律,建立出以网损最小为目标的网络均匀性条件;其次,提出均匀网络逼近算法解决电源出力的约束问题;最后,结合提出的高效单环网重构算法,给出考虑电容器投切、重构和可调度式电源优化的配电网协调优化算法。算例首先验证了均匀网络逼近算法的准确性,在此基础上,采用IEEE33标准节点系统验证了方法的高效性和实用性。     

水库群优化调度中的多层次分解组合优化算法

随机动态规划(SDP)在水库群优化调度中会导致“维数灾”问题,也难应用于多年调节水库。提出了一种水库群优化调度的多层次分解组合优化算法,其包括应用于多年调节水库SDP操作的均匀下泄流量算法,把库群优化问题分解为第1层次的单库SDP优化,然后应用改进的遗传模拟退火算法(GASA)对单库结果进行第2层次的组合优化。在贵州乌江梯级水库群中长期发电优化调度研究中,获得库群多年平均发电量94.72×108 kW·h,大于GA、SA和SDP单独运用的结果,运行速度也较SDP快。结果说明,提出的多层次分解组合优化算法是一种新的有希望的水库群优化调度方法。     

A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem

Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.     

一种免疫补体优化算法

针对目前提出的免疫优化算法在求解优化问题时还存在收敛速度慢,往往不能求得最优解,鲁棒性低的问题,基于生物免疫补体激活原理,提出了一种免疫补体优化算法。在算法中,依据补体激活理论,设计了主要的补体算子：分裂算子和结合算子,并根据补体激活过程,通过补体算子的作用对问题解不断优化,求得全局最优解。最后对算法的收敛性和鲁棒性进行了理论分析,并将免疫补体优化算法与典型的克隆选择算法进行了对比实验。理论与实验结果表明了免疫补体优化算法是收敛的,并且收敛速度更快,求得的最优解更好,鲁棒性更高。     

Immune response-based algorithm for optimization of dynamic environments

A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle. The dynamic process of artificial immune response with operators such as immune cloning, multi-scale variation and gradient-based diversity was modeled. Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens, a sigmoid model that can clearly describe clonal proliferation was proposed. In addition, with the introduction of multiple populations and multi-scale variation, the algorithm can well maintain the population diversity during the dynamic searching process. Unlike traditional artificial immune algorithms, which require randomly generated cells added to the current population to explore its fitness landscape, AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments. Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks. Preliminary experiments show that AIDE can maintain high population diversity during the search process, simultaneously can speed up the optimization. Thus, AIDE is useful for the optimization of dynamic environments.     

克隆选择单变量边缘分布算法

张庆彬，吴惕华，刘波针对单变量边缘分布算法（UMDA）求解复杂优化问题的局限性，将人工免疫系统引入分布估计算法（EDAs）领域，提出了一种基于克隆选择原理的单变量边缘分布算法.该算法在进化过程中的每一代执行若干次克隆选择算法（CLONALG），利用克隆选择过程中的高频变异操作提高混合算法的局部搜索能力.通过对2种不同旅行商问题（TSP）的仿真实验表明，与UMDA、CLONALG以及UMDA和2 opt局部搜索算法的混合算法(UMDA2 opt)相比，克隆选择单变量边缘分布算法具有更高的优化性能.     

基于动态规划优化传感器网络寿命的算法

根据多跳无线传感器网络的特点,为了优化网络中节点的生存时间,提出了一种求解无线传感器网络寿命Pareto最优的集中式算法.熵是系统平均程度的度量,通过证明最大熵函数与传感器网络寿命Pareto最优的等价关系,建立了求解传感器网络最大熵函数的动态规划模型,将复杂的多目标线性规划问题转换成单目标动态规划问题.理论分析和仿真研究结果表明,新算法能够快速有效地获得网络寿命的Pareto最优解,达到了优化传感器网络寿命的目的,提高了系统的可实现性并降低了计算复杂度.     

求解协同干扰问题的高效免疫遗传算法

为协同干扰武器目标分配问题建立的数学模型,当问题规模增大时,现有的智能求解算法表现出两点不足,一是所求解质量下降;二是求解速度不可接受。针对该两点不足提出了具有贪婪修复过程的免疫遗传算法,算法设计了通用十进制扩展编码方案、基于免疫的轮盘赌选择算子和贪婪修复算子。仿真实验表明,该算法与现有算法相比具有明显的效率优势,在解决大规模协同干扰武器目标分配问题时不仅解算时间可接受而且所求解质量比同类算法高。     

基于蚁群算法的多目标优化问题研究

为保持所求得的多目标优化问题Pareto最优解的多样性,文章提出了一种新的蚁群算法。选择策略采用多信息素权重,信息素更新结合了局部信息素更新与全局信息素更新。其中,全局信息素更新采用了两个最好解。此外,通过在外部设置外部集来存储Pareto解,并将改进的算法应用在双目标TSP上。最后进行了仿真实验,结果表明新方法比NSGA-II和SPEA2更有效。     

A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach

Portfolio selection is one of the major capital allocation and budgeting issues in financial management, and a variety of models have been presented for optimal selection. Semi-variance is usually considered as a risk factor in drawing up an efficient frontier and the optimal portfolio. Since semi-variance offers a better estimation of the actual risk portfolio, it was used as a measure to approximate the risk of investment in this work. The optimal portfolio selection is one of the non-deterministic polynomial(NP)-hard problems that have not been presented in an exact algorithm, which can solve this problem in a polynomial time. Meta-heuristic algorithms are usually used to solve such problems. A novel hybrid harmony search and artificial bee colony algorithm and its application were introduced in order to draw efficient frontier portfolios. Computational results show that this algorithm is more successful than the harmony search method and genetic algorithm. In addition, it is more accurate in finding optimal solutions at all levels of risk and return.     

一种新的差分进化约束优化算法

对于约束优化问题,目前提出的差分进化算法大多采用罚函数法,但此方法对罚参数有很强的依赖性.基于此,把约束优化问题中的约束条件当作一个目标函数,从而把约束优化问题转化为有两个目标函数的多目标优化问题.借鉴多目标优化中的Pareto的概念,对种群中的个体规定等级,便于在优胜劣汰过程中确定选择概率.同时,在算法陷入局部最优时,采用一种不可行解替换机制来提高算法搜索能力.对13个标准测试问题的测试结果表明,与动态惩罚函数的进化算法、可行性规则的差分进化算法、采用随机排序的进化策略以及人工免疫响应约束进化策略相比,新算法在求解精度上均具有一定的优势.     

Optimal design of dynamic and control performance for planar manipulator

k]A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed. After the kinematic and dynamic analysis, several advantages of the mechanism were illustrated, which made it possible to obtain good dynamic and control performances just through mechanism optimization. Based on the idea of design for control (DFC), a novel kind of multi-objective optimization model was proposed. There were three optimization objectives: the index of inertia, the index describing the dynamic coupling effects and the global condition number. Other indexes to characterize the designing requirements such as the velocity of end-effector, the workspace size, and the first mode natural frequency were regarded as the constraints. The cross-section area and length of the linkages were chosen as the design variables. NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem. Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets. Finally, experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms. A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed. The results show that the optimized mechanism has an approximate decoupled dynamics structure, and each active joint can be regarded as a linear SISO system. The control performances of the linear and nonlinear controllers were also compared. It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.