几种优化算法在浅海匹配场反演中的性能比较

在分析模拟退火算法、遗传算法、差异进化算法、下山单纯形差异进化算法的优化机理的基础上,定量比较了上述算法在浅海匹配场反演中的效率差异。模拟退火算法与遗传算法只使用目标函数值信息在参数空间搜索全局最优值,效率低且易受参数间耦合的影响。差异进化算法使用种群中个体间的距离与方位信息在参数空间中搜索全局最优值,优化效率随着优化过程的进行而下降。下山单纯形差异进化算法将下山单纯形算法融入差异进化算法,增强了差异进化算法的寻优能力,混合算法对目标函数梯度信息敏感的特性使得这一算法具有较强的解耦能力。浅海匹配场反演仿真算例从最优参数反演结果、最终目标函数值、反演时间等方面检验了上述算法的反演效率。     

Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.     

混合遗传算法在结构动力反演中的应用研究

研究了输入荷载未知条件下的结构参数识别及荷载反演问题,该问题最终归结为一个非线性的优化问题求解,根据目标函数、约束条件的具体特性,采用BFGS算法作为局部搜索算子,构造了基于浮点编码的混合遗传算法.针对系统输入未知的激励特性,采用分解反演的计算策略,从而提高了动力反演中混合遗传算法的稳健性和收敛速度.数值算例表明,这种方法具有很好的参数识别精度及荷载反演效果,对测试噪声有较强的适应能力.     

A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking

Batch processing machines (BPMs) have important applications in a variety of industrial systems. This paper considers the problem of scheduling two BPMs in a flow shop with arbitrary release times and blocking such that the makespan is minimised. The problem is formulated as a mixed integer programming model. Subsequently, a hybrid discrete differential evolution (HDDE) algorithm is proposed. In the algorithm, individuals in the population are first represented as discrete job sequences, and mutation and crossover operators are applied based on the representation. Second, after using the first-fit rule to form batches, a novel least idle/blocking time heuristic is developed to schedule the batches in the flow shop. Furthermore, an effective local search technique is embedded in the algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by comparing its results to a commercial solver (CPLEX), a genetic algorithm and a simulated annealing algorithm. Computational experiments demonstrate the superiority of the HDDE algorithm in terms of solution quality, robustness and run time.     

遗传禁忌搜索算法在混流装配线排序中的应用

针对混流装配线排序问题，提出了一种混合遗传禁忌搜索算法，在每一代遗传演化之后，按一定比例随机选择部分解进行禁总搜索，以提高算法的全局搜索能力和收敛性。通过一个混流装配线排序实验，分别利用遗传算法和遗传禁忌搜索算法进行求解，结果表明遗传禁忌搜索算法具有更好的全局搜索能力和收敛性能。     

Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times

This paper presents an efficient hybrid metaheuristics for scheduling jobs in a hybrid flowshop with sequence-dependent setup times. The problem is to determine a schedule that minimises the sum of earliness and tardiness of jobs. Since this problem class is NP-hard in the strong sense, there seems to be no escape from appealing to metaheuristic procedures to achieve near-optimal solutions for real life problems. This paper proposes the hybrid metaheuristic algorithm which comprises three components: an initial population generation method based on an ant colony optimisation, a simulated annealing algorithm as an evolutionary algorithm that employs certain probability to avoid becoming trapped in a local optimum, and a variable neighbourhood search which involves three local search procedures to improve the population. A design of experiments approach is employed to calibrate the parameters of the algorithm. Results of computational tests in solving 252 problems up to 100 jobs have shown that the proposed algorithm is computationally more effective in yielding solutions of better quality than the adapted random key genetic algorithm and immune algorithm presented previously.     

基于改进遗传算法的水轮发电机振动荷载参数识别

根据水轮发电机现场振动测试实验数据,采用改进的遗传算法研究了水轮发电机运行过程中振动荷载反演问题。与传统的参数反演方法相比,遗传算法并不是基于对目标函数梯度方向搜索,而是在解的整个区域随机搜索.将遗传算法与模拟退火算法相结合,提高了种群在进化过程中个体多样性,可以有效地防止简单遗传算法早熟问题。同时,将遗传算法与梯度优化方法相结合,使得混合型遗传算法有效地解决了梯度算法局部极小问题和简单遗传算法的收敛速度慢问题。工程实际应用表明,采用本文所建立改进遗传算法所反演的水轮发电机振动荷载参数,预报其它振动观测点的位移具有较高的预报精度。     

Multidisciplinary design and optimization of an air launched satellite launch vehicle using a hybrid heuristic search algorithm

A multidisciplinary design and optimization strategy for a multistage air launched satellite launch vehicle comprising of a solid propulsion system to low earth orbit with the implementation of a hybrid heuristic search algorithm is proposed in this article. The proposed approach integrated the search properties of a genetic algorithm and simulated annealing, thus achieving an optimal solution while satisfying the design objectives and performance constraints. The genetic algorithm identified the feasible region of solutions and simulated annealing exploited the identified feasible region in search of optimality. The proposed methodology coupled with design space reduction allows the designer to explore promising regions of optimality. Modules for mass properties, propulsion characteristics, aerodynamics, and flight dynamics are integrated to produce a high-fidelity model of the vehicle. The objective of this article is to develop a design strategy that more efficiently and effectively facilitates multidisciplinary design analysis and optimization for an air launched satellite launch vehicle.     

基于改进遗传算法的海底地层参数反演计算

为了提高对海底地层参数变量的反演计算能力,设计了一种基于双种群协同进化策略的改进遗传算法。针对标准遗传算法局部搜索能力差且易于出现早熟现象的缺点,在标准遗传算法基础上引入双种群同时进行优化搜索,两个种群分别给予不同的控制参数,实现协同进化,最终给出一个综合的最优解。通过两个算例对遗传算法的寻优能力进行测试,实验结果表明,提出的改进算法不仅提高了搜索性能,并且对遗传控制参数的依靠度大大降低,特别是对大型复合参数反演问题的求解计算更为有效。     

Reducing the computational cost of local search in the hybrid evolutionary algorithm with application to electronic imaging

This article focuses on the efficiency problems associated with the use of local search in the hybrid evolutionary algorithm. A two-phase cyclic local search is proposed that alternates the random search and the downhill simplex method (DSM), and helps prevent the algorithm from converging to a sub-optimal solution in multidimensional optimization. The algorithm utilizes a novel micro-model of image local response, in order to reduce the number of fitness evaluations during the local DSM search, with the application to the global optimization problem arising in electronic imaging. The problem is stated as the search for the feasible transformation parameters that minimize the difference between two images. Image local response is defined as the variation of the fitness function that occurs because of a small variation of the parameters, and is computed over a small pixel area. The computed response coefficients specify a contraction transformation applied to the vector of the regular DSM coefficients that control the movement and the shape of the simplex. The transformation adjusts the length of the vector, making the step size of the simplex adaptive to the local properties of the fitness landscape. The computational experiments with two-dimensional grayscale images provide the experimental support and justification of the analytical model of image local response and its utilization for the reduction of the computational cost of local search, without the loss of the quality of the final solution.     

Truss topology,shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution

A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article. Hybrid reproduction and control parameter adaptation strategies are employed to increase the performance of the algorithm. The proposed algorithm, called fully stressed design–grey wolf–adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems. The problems have mixed continuous/discrete design variables that are assigned as simultaneous topology, shape and sizing design variables. FSD-GWADE provides competitive results and gives superior results at a higher success rate than the previous FSD-based algorithm.     

Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale

The objective of this work was to develop an optimization strategy for the design of pharmaceutical formulations. The mixed strategy was used to optimize a dry powder blend containing 500 mg of alpha methyl dopa to be filled into hard gelatin capsules. The experimental plan consisted of assessing blend flow and dissolution rate using formulations manufactured at small laboratory scale, selecting the optimum formulation, and confirming the data. Two optimization techniques were used in the solid pharmaceutical product design: a genetic algorithm (GA) and a downhill simplex technique. The genetic algorithm used in this work was implemented in an interactive form. Data for each generation of formulations were introduced to the computer with the corresponding values of a fitness function, which was determined in experimental form for each individual formulation. The fitness function used to evaluate product performance (capsule) was defined in terms of the dissolution rate multiplied by a weight function that penalizes those formulations with flow index outside a predefined range. The formulation design contained variable concentrations and types of lubricants/glidants. There were 64 combinations of seven agents with discrete ranges of concentrations codified into a 16-bit chromosome. Crossing and mutation operations were implemented with relatively high probabilities, for generations with a relatively small number of individuals, due to the restrictions imposed by the experimental cost. The mixed formulation strategy based on genetic algorithms and downhill simplex was used to obtain sequentially improved formulations based on two desired targets: in vitro dissolution rate and flow properties. The basic downhill simplex method was used to obtain an optimal formulation on the regression response surface obtained from the genetic algorithm data. The results obtained in this work clearly illustrate the potential of the proposed mixed optimization strategy to obtain optimal formulations.     

Application of a Mixed Optimization Strategy in the Design of a Pharmaceutical Solid Formulation at Laboratory Scale

The objective of this work was to develop an optimization strategy for the design of pharmaceutical formulations. The mixed strategy was used to optimize a dry powder blend containing 500 mg of alpha methyl dopa to be filled into hard gelatin capsules. The experimental plan consisted of assessing blend flow and dissolution rate using formulations manufactured at small laboratory scale, selecting the optimum formulation, and confirming the data. Two optimization techniques were used in the solid pharmaceutical product design: a genetic algorithm (GA) and a downhill simplex technique. The genetic algorithm used in this work was implemented in an interactive form. Data for each generation of formulations were introduced to the computer with the corresponding values of a fitness function, which was determined in experimental form for each individual formulation. The fitness function used to evaluate product performance (capsule) was defined in terms of the dissolution rate multiplied by a weight function that penalizes those formulations with flow index outside a predefined range. The formulation design contained variable concentrations and types of lubricants/glidants. There were 64 combinations of seven agents with discrete ranges of concentrations codified into a 16-bit chromosome. Crossing and mutation operations were implemented with relatively high probabilities, for generations with a relatively small number of individuals, due to the restrictions imposed by the experimental cost. The mixed formulation strategy based on genetic algorithms and downhill simplex was used to obtain sequentially improved formulations based on two desired targets: in vitro dissolution rate and flow properties. The basic downhill simplex method was used to obtain an optimal formulation on the regression response surface obtained from the genetic algorithm data. The results obtained in this work clearly illustrate the potential of the proposed mixed optimization strategy to obtain optimal formulations.     

基于改进混合遗传细菌觅食算法的贝叶斯结构学习算法

针对利用启发式学习算法学习贝叶斯网络时容易陷入局部最优和寻优效率低的问题,提出一种改进的混合遗传细菌觅食优化算法的贝叶斯网络结构学习算法。该算法首先通过遗传算法求得较优种群并作为细菌觅食算法的初始种群;然后利用交叉和变异策略改进细菌觅食算法的复制行为,增加种群多样性,扩大搜索空间;最后通过改进细菌觅食算法的迁移行为的初始化操作更新种群,防止精英个体的丢失。通过种群的迭代搜索最终获得最优的贝叶斯网络结构。实验仿真结果表明,与其他算法相比,该算法的收敛精度和效率有所提升。     

Strain Measurement of Nickel Thin Film by a Digital Image Correlation Method

Abstract:  Digital image correlation is a whole-field and non-contact strain-measuring method. It provides deformation information of a specimen by processing two digital images captured before and after the deformation. To search the deformed images, a hybrid genetic algorithm, in which a simulated annealing mutation process and adaptive mechanisms are combined with a real-parameter genetic algorithm, is adopted. This method is used to measure the strain during the microtensile testing of nickel thin film. In addition to the conventional single region, a double region in which the strain is inferred from the distance change of two regions is proposed to calculate the strain by digital image correlation. The results indicate that while the strain values obtained by single-region method are reasonable, those obtained by the double region method are more accurate. Moreover, the mechanical properties of nickel thin film could be obtained.     

基于实时交通信息的灾后应急物流多源配送优化问题

针对自然灾害后的应急调度与配送问题,以三级多源配送系统为研究对象,考虑实时交通信息更新对配送方案的影响,构建了基于公平分配满足度水平最大的数学模型,并设计了嵌入禁忌搜索的混合遗传算法对问题进行求解。结果表明,实时交通信息相较于静态情景具有更高的救灾满意度和更快的响应速度。     

基于空间收缩的并行演化算法

提出了一种基于空间收缩的求解MINLP问题的新算法。算法应用了快速有效的不完全演化搜索较优解的分布信息，通过分布信息定位最优解的可能分布，再由精英个体信息决定下次搜索空间。仿真结果表明该算法在搜索效率、应用范围、解的精确性和鲁棒性上都优于其他现存演化算法。     

基于改进蝠鲼优化算法的光伏组件参数辨识模型

为了解决当前光伏组件模型中存在的参数辨识精度低和稳定性差的问题,提出了一种基于折射学习机制的蝠鲼觅食优化算法的三二极管光伏组件参数辨识模型(RLMRFO-TDM)。该模型将差分进化机制融入到MRFO算法的种群更新环节,提高了MRFO算法的局部探索能力,并加快了MRFO算法收敛速度;引入折射学习机制改善了MRFO算法的随机性,提高了种群在搜索区域中的离散性和MRFO算法的全局搜索能力。利用基准测试函数,验证了RLMRFO算法的有效性;采用STP6-120/36和STM6-40/36两种光伏组件的数据集对RLMRFO-TDM模型的参数辨识进行性能测试,与其他模型相比,RLMRFO-TDM模型的辨识精度、稳定性以及收敛速度表现最优。     

Comparison between hybridized algorithm of GA–SA and ABC,GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

小生境遗传算法在反演堤坝地震勘探数据中的应用

基于反演问题的不确定性和目标函数的多峰性,本文提出引入小生境遗传算法,采用自适应控制交配概率和变异概率,求解出目标函数的若干个局部峰(或全局峰),然后利用先验知识,判定得到满意解。本文对实测资料进行了层速度与厚度的反演,通过实际值与反演值的比较,验证了进行多峰优化的有效性。