一种混沌差分进化和粒子群优化混合算法*

为了改善差分进化粒子群算法的局部搜索能力和收敛速度,提出了一种混沌差分进化的粒子群优化算法。该算法利用信息交换机制将两组种群分别用差分进化算法和粒子群算法进行协同进化,并且将混沌变异操作引入其中,加强算法的局部搜索能力。通过对三个标准函数进行测试,仿真结果表明该算法与DEPSO算法相比,全局搜索能力、抗早熟收敛性能及收敛速度大大提高。     

基于DEPSO 的模糊时间ZW多产品厂间歇调度

研究以最小化完工时间为目标的模糊加工时间零等待多产品厂间歇调度问题, 提出一种基于差分进化粒子群优化(DEPSO) 的间歇调度算法. 以基本粒子群算法为整体进化框架, 采用基于反向学习的方法初始化种群, 引入群体极值保持代数作为阈值, 利用基于排序的差分进化算法优化粒子个体极值位置, 改变粒子的搜索范围, 防止粒子陷入局部极值. 仿真实验验证了所提算法在解决模糊加工时间零等待多产品厂间歇调度问题上的有效性和优越性.

     

求解模糊相关机会规划模型的混合差分进化算法

针对不确定规划领域中存在的模糊相关机会规划模型,基于群体智能的差分进化算法,设计一种新的求解模糊相关机会规划模型的混合智能算法．该算法基于粒子群优化算法对差分进化算法进行改进,并运用模糊模拟技术对模糊相关机会规划模型进行分析和数值求解,无需像传统的基于遗传算法的混合智能算法需要很长时间并经过复杂的计算才能得到合理的结果．最后,通过实例表明了所提混合智能算法的合理性和有效性．     

一种新的混合智能极限学习机

提出一种基于差分进化(DE)和粒子群优化(PSO)的混合智能方法—–DEPSO算法,并通过对10个典型函数进行测试,表明DEPSO算法具有良好的寻优性能。针对单隐层前向神经网络(SLFNs)提出一种改进的学习算法—–DEPSO-ELM算法,即应用DEPSO算法优化SLFNs的隐层节点参数,采用极限学习算法(ELM)求取SLFNs的输出权值。将DEPSO-ELM算法应用于6个典型真实数据集的回归计算,并与DE-ELM、SaE-ELM算法相比,获得了更精确的计算结果。最后,将DEPSO-ELM算法应用于数控机床热误差的建模预测,获得了良好的预测效果。     

差分进化微粒群优化算法-DEPSO

微粒群优化算法是一种新的进化计算技术,具有良好的优化性能,但是对于高维多模态函数,因进化后期微粒多样性的降低导致算法早熟收敛.文章提出的差分进化微粒群优化算法(DEPSO),拓宽了微粒信息传递的途径,增加了微粒的多样性,保证了算法的全局收敛.实验结果表明,DEPSO比PSO有更好的性能.     

差分进化混合粒子群算法求解装配式住宅项目进度优化问题

针对装配式住宅项目进度优化问题,提出了基于差分算法(DE)和粒子群算法(PSO)的差分粒子群混合算法(DEPSO)。建立了以项目工期最优为目标的进度优化模型,通过在DE和PSO之间建立信息交流机制,避免了单一算法容易落入局部最优和精度低的缺陷。最后以某装配式住宅项目为例,通过三种算法的比较,结果表明DEPSO在求解装配式住宅项目进度优化中合理高效、鲁棒性较强,能有效地解决装配式住宅项目工期优化问题,有较大的应用价值。     

基于差分进化的离散粒子群算法求解TSP问题

针对TSP问题,结合离散粒子群算法和差分进化算法各自的特点,提出了基于差分进化的离散粒子群算法。该算法先利用差分进化算法的变异、选择算子产生新的群体,再通过离散粒子群算法和交叉及选择算子进行局部搜索。通过对标准的30个城市进行实验,实验结果表明,该优化算法在求解TSP问题上有很好的性能。     

基于DEPSO算法的多无人机绕检机队规模优化方法

为提高民航客机航前航后绕检效率、减少人工成本,研究无人机绕检时多机协作的机队规模优化算法。从多机协作的航迹规划出发,构建客机外观绕检模型,并采用栅格法对绕检作业空间进行离散化;建立航迹规划的约束条件,设计航迹规划代价函数;采用基于差分进化粒子群算法(DEPSO)对机队规模优化,引入差分进化更新粒子群,通过自适应方法调整粒子的惯性权重。仿真结果表明,所研究的方法可获得代价函数指标下的最佳机队规模。     

一种自适应交替的差分混合蛙跳优化算法

针对混合蛙跳算法在解决高维优化问题时易早熟收敛、求解精度低等问题,提出一种自适应交替的差分混合蛙跳优化算法。采用粒子群算法在短时间内产生一组满足约束条件的初始解,以提高初始解的质量。在此基础上,利用差分进化算法全局搜索能力强、种群多样性好等优点,设计一种自适应选择机制,动态地交替使用混合蛙跳算法和差分进化算法,使两者有机融合、优势互补。对6个经典函数的仿真测试结果表明,该算法可以丰富粒子的多样性,使算法前期和后期都具有较好的寻优能力,且寻优速率、求解精度、稳定性都优于混合蛙跳算法、差分进化算法和差分混合蛙跳算法。     

混合差分变异策略

为了改善差分进化算法的求解性能,提出一种新的混合差分变异策略．该策略将种群中的每一个个体视作带电粒子,利用粒子所带的电荷量以及粒子之间的吸引排斥机制确定个体移动方向和位移大小．该策略会使个体在其他3个个体施加于它的力的方向上自适应地移动．数值实验表明基于该策略的差分进化算法求解精度高、评估次数少．     

差分进化粒子群混合优化算法的研究与应用

对基本粒子群算法（PSO）和差分进化算法（DE）进行了分析,有机结合两种进化算法提出了一种新型差分进化粒子群混合优化算法,该算法将优化过程分成两阶段,两分群分别采用PSO算法和DE算法同时进行。迭代过程中引入进化速度因子并通过群体间的信息交流阻止算法陷入局部最优。对4个高维复杂函数寻优测试表明算法的鲁棒性、收敛速度和精度,全局搜索能力均优于常规PSO和DE。将提出的改进算法用于乙烯收率软测量建模,应用结果表明模型精度较高、泛化性能较好。     

An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem

The p-hub center problem is useful for the delivery of perishable and time-sensitive system such as express mail service and emergency service. In this paper, we propose a new fuzzy p-hub center problem, in which the travel times are uncertain and characterized by normal fuzzy vectors. The objective of our model is to maximize the credibility of fuzzy travel times not exceeding a predetermined acceptable efficient time point along all paths on a network. Since the proposed hub location problem is too complex to apply conventional optimization algorithms, we adapt an approximation approach (AA) to discretize fuzzy travel times and reformulate the original problem as a mixed-integer programming problem subject to logic constraints. After that, we take advantage of the structural characteristics to develop a parametric decomposition method to divide the approximate p-hub center problem into two mixed-integer programming subproblems. Finally, we design an improved hybrid particle swarm optimization (PSO) algorithm by combining PSO with genetic operators and local search (LS) to update and improve particles for the subproblems. We also evaluate the improved hybrid PSO algorithm against other two solution methods, genetic algorithm (GA) and PSO without LS components. Using a simulated data set of 10 nodes, the computational results show that the improved hybrid PSO algorithm achieves the better performance than GA and PSO without LS in terms of runtime and solution quality.     

基于改进的粒子群算法和信息熵的知识获取方法

针对粒子群优化算法（PSO）易陷入局部优化的问题,在PSO算法加入交叉变异算子,克服了标准PSO算法易陷入局部最优的不足;并将改进的PSO算法和模糊C 均值聚类相结合,提出了一种新的模糊聚类算法CMPSO FCM,该算法具有良好的搜索能力和聚类效果。进而将聚类得到的属性隶属矩阵用于属性约简,并提出一种基于信息熵的模糊粗糙集知识获取的方法。实验和实例分析表明该方法的正确性和有效性。     

基于微粒群与混合蛙跳融合的群体智能算法

针对微粒群算法和混合蛙跳算法存在的早熟收敛问题,提出一种基于微粒群与混合蛙跳算法融合的群体智能算法。新算法将整个群体分成数目相等的蛙群和微粒群群体。在两群体独立进化过程中,设计了一种两群之间的信息替换策略：比较蛙群与微粒群的最佳适应值,如果蛙群进化较好,利用蛙群各子群中最差个体替换微粒群一部分较好个体;否则,用微粒群中较好的一部分个体替换蛙群各子群的最好个体。同时,设计了一种两群之间的相互协作方式。为避免微粒群因早熟收敛而影响信息替换策略效果,适时对其所有个体最好位置进行随机扰动。仿真实验表明,新算法可以有效提高全局搜索能力及收敛速度,对于高维复杂函数问题,算法具有很好的稳定性。     

A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers.     

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)     

Fuzzy fixed charge solid transportation problem and algorithm

This paper mainly investigates the fixed charge solid transportation problem under fuzzy environment, in which the direct costs, the fixed charges, the supplies, the demands and the conveyance capacities are supposed to be fuzzy variables. As a result, several new models, i.e., expected value model, chance-constrained programming model and dependent-chance programming model, are constructed on the basis of credibility theory. After that, the crisp equivalences are also discussed for different models. In order to solve the models, hybrid intelligent algorithm is designed based on the fuzzy simulation technique and tabu search algorithm. Finally, two application results are given to show the applications of the models and algorithm.     

多阈值图像分割的模糊粒子群优化算法

把粒子群算法应用到多阈值图像分割中,结合已有的模糊C-均值聚类法提出了一种基于模糊技术的粒子群优化多阈值图像分割算法。FCM聚类算法是一种局部搜索算法,对初始值较为敏感,容易陷入局部极小值而不能得到全局最优解。PSO算法是一种基于群体的具有全局寻优能力的优化方法。将FCM聚类算法和PSO算法结合起来,将FCM聚类算法的聚类准则函数作为PSO算法中的粒子适应度函数。仿真实验表明新算法在最大熵评判准则下能够得到最优阈值。