A surrogate-assisted particle swarm optimization algorithm based on efficient global optimization for expensive black-box problems

Evolutionary algorithms cannot effectively handle computationally expensive problems because of the unaffordable computational cost brought by a large number of fitness evaluations. Therefore, surrogates are widely used to assist evolutionary algorithms in solving these problems. This article proposes an improved surrogate-assisted particle swarm optimization (ISAPSO) algorithm, in which a hybrid particle swarm optimization (PSO) is combined with global and local surrogates. The global surrogate is not only used to predict fitness values for reducing computational burden but also regarded as a global searcher to speed up the global search process of PSO by using an efficient global optimization algorithm, while the local one is constructed for a local search in the neighbourhood of the current optimal solution by finding the predicted optimal solution of the local surrogate. Empirical studies on 10 widely used benchmark problems and a real-world structural design optimization problem of a driving axle show that the ISAPSO algorithm is effective and highly competitive.     

基于粒子群与聚类的多目标优化算法

针对粒子群优化算法容易陷入局部最优的问题,提出了一种基于粒子群优化与分解聚类方法相结合的多目标优化算法。算法基于参考向量分解的方法,通过聚类优选粒子策略来更新全局最优解。首先,通过每条均匀分布的参考向量对粒子进行聚类操作,来促进粒子的多样性。从每个聚类中选择一个具有最小聚合函数适应度值的粒子,以平衡收敛性和多样性。动态更新全局最优解和个体最优解,引导种群均匀分布在帕累托前沿附近。通过仿真实验,与4种粒子群多目标优化算法进行对比。实验结果表明,提出的算法在27个选定的基准测试问题中获得了20个反世代距离(IGD)最优值。     

Concurrent Implementation of the Optimal Incremental Approximation Method for the Adaptive and Meshless Solution of Differential Equations

The optimal incremental function approximation method is implemented for the adaptive and meshless solution of differential equations. The basis functions and associated coefficients of a series expansion representing the solution are selected optimally at each step of the algorithm according to appropriate error minimization criteria. Thus, the solution is built incrementally. In this manner, the computational technique is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a posteriori error estimates. Since the basis functions are associated with the nodes only, the method can be viewed as a meshless method. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems. Complicated data structures, expensive remeshing algorithms, and systems solvers are avoided. Computational efficiency is increased by using low-order local basis functions and the parallel direct search (PDS) optimization algorithm. Numerical results are reported for both a linear and a nonlinear problem associated with fluid dynamics. Challenges and opportunities regarding the use of this method are discussed.     

Cross-cumulative residual entropy-based medical image registration via hybrid differential search algorithm

Image registration is the process of overlaying images of the same scene taken at different times by different sensors from different viewpoints. The cross-cumulative residual entropy (CCRE)-based medical image registration could achieve a high precision and a strong robustness performance. However, the optimization problem formulated by CCRE consists of some local extrema, especially for noise images. In order to address these difficulties, this article proposes a new optimization algorithm named hybrid differential search algorithm (HDSA) to optimize CCRE. As HDSA consists of simple control parameters, it is independent of the initial searching point. In addition, HDSA ameliorated the search method and the iterative conditions. As a result, the optimization process is more stable and efficient. Image registration experiments of HDSA are performed and compared with the conventional differential search algorithm (DSA) and adaptive differential evolution with optional external archive (JADE). The results show that HDSA does not only overcome the difficulties of sticking in the local extrema but also enhances the precision of registration. It is effective, robust, and fast for both the single-mode rigid medical image registration and the multispectral-mode rigid medical image registration.     

基于Retinex的弱可见光和红外图像融合算法

目的 针对目前弱可见光与红外图像融合后的图像仍存在细节大量丢失、目标模糊不清的问题,提出一种基于Retinex对弱可见光图像进行增强预处理后,再基于NSST和SWT变换进行图像融合的算法。方法 首先用SSR对弱可见光图像进行增强处理,增强后的可见光和红外图像进行NSST分解得到第1次的高低频系数,高频系数采用基于局部能量特征的方法进行融合;低频系数经过SWT分解得到第2次高低频系数,第2次的高频系数采用同样的方法融合,低频系数采用线性加权方法融合,然后将第2次高低频的融合结果经过SWT逆变换得到新的低频系数。最后把第1次高频系数融合结果和新的低频系数进行NSST逆变换得到融合图像。结果 通过仿真实验,将文中算法与NSST,NSCT以及文献[5]算法进行对比,结果表明主观视觉上融合图像细节更加清晰,客观评价上,平均梯度、空间频率（SF）、标准差、信息熵、边缘信息保留量等指标分别提高了35.63%,26.73%,16.89%,7.2%,4.6%。结论 文中算法对图像融合有较好的改善作用,融合图像的可视性和图像质量都得到显著提高。     

满足“一刀切”要求的木工板排样优化研究

 针对木工板手工排样效率低和材料利用率低问题,提出木工板“一刀切”排样优化算法．在剩余矩形填充算法中添加启发式分块原则,改进的剩余矩形填充算法满足“一刀切”工艺要求．采用遗传算法对矩形件进行排样优化,以提高木工板利用率,降低企业生产成本．为提高算法的优化精度,使用基于指数变换的非线性动态适应度函数,引入精英保护策略,应用部分填充交叉（partially matched crossover）算子．结合剩余矩形填充“一刀切”算法对遗传种群进行解码计算原料利用率,并作为适应度函数值,进行迭代搜索最优解．排样实例表明木工板“一刀切”排样优化算法能够很好地解决多品种大规模木工板排样问题．     

Elephant swarm water search algorithm for global optimization

The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. Evolutionary computation and metaheuristics based on swarm intelligence are very popular nature-inspired optimization techniques. In this paper, the author has proposed a novel elephant swarm water search algorithm (ESWSA) inspired by the behaviour of social elephants, to solve different optimization problems. This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought. Initially, we perform preliminary parametric sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values in real-life problems. In addition, the algorithm is evaluated against a number of widely used benchmark functions for global optimizations, and it is observed that the proposed algorithm has better performance for most of the cases compared with other state-of-the-art metaheuristics. Moreover, ESWSA performs better during fitness test, convergence test, computational complexity test, success rate test and scalability test for most of the benchmarks. Next, ESWSA is tested against two well-known constrained optimization problems, where ESWSA is found to be very efficient in term of execution speed and best fitness. As an application of ESWSA to real-life problem, it has been tested against a benchmark problem of computational biology, i.e., inference of Gene Regulatory Network based on Recurrent Neural Network. It has been observed that the proposed ESWSA is able to reach nearest to global minima and enabled inference of all true regulations of GRN correctly with less computational time compared with the other existing metaheuristics.     

求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

一类禁忌算法及其在医学图像配准中的应用

针对脑功能成像中图像配准的优化模型,提出了一种基于记忆的禁忌算法。数值试验表明该算法是可行的、有效的。应用此算法求解医学图像配准的优化模型,可实现时间序列脑功能图像的高精度配准。     

A local-best harmony search algorithm with dynamic subpopulations

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.     

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.     

Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.     

Production scheduling optimization algorithm for the hot rolling processes

The hot rolling production scheduling problem is an extremely difficult and time-consuming process, so it is quite difficult to achieve an optimal solution with traditional optimization methods owing to the high computational complexity. To ensure the feasibility of solutions and improve the efficiency of the scheduling, this paper proposes a vehicle routing problem (VRP) to model the problem and develops an easily implemented hybrid approach (QPSO-SA) to solve the problem. In the hybrid approach, quantum particle swarm optimization (QPSO) combines local search and global search to search the optimal results and simulated annealing (SA) employs certain probability to avoid getting into a local optimum. The computational results from actual production data have shown that the proposed model and algorithm are feasible and effective for the hot rolling scheduling problem.     

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.     

基于互信息和混合算法的印刷图像配准

目的为了提高印刷图像配准的精度,提出一种基于混合搜索算法的图像配准方法。方法首先求取图像的归一化互信息,然后利用GA算法(遗传算法)进行全局搜索,得出粗配准参数;最后,利用Powell算法进行局部寻优,得出精配准参数。结果混合算法的配准结果与只用单一Powell搜索算法或只用单一GA搜索算法相比,在各个几何变换方向上得到了更小的配准误差。结论与GA算法和Powell算法相比,文中建议的混合算法配准精确度更高、速度更快。     

A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of truss structures

In this paper a new graph-based evolutionary algorithm, gM-PAES, is proposed in order to solve the complex problem of truss layout multi-objective optimization. In this algorithm a graph-based genotype is employed as a modified version of Memetic Pareto Archive Evolution Strategy (M-PAES), a well-known hybrid multi-objective optimization algorithm, and consequently, new graph-based crossover and mutation operators perform as the solution generation tools in this algorithm. The genetic operators are designed in a way that helps the multi-objective optimizer to cover all parts of the true Pareto front in this specific problem. In the optimization process of the proposed algorithm, the local search part of gM-PAES is controlled adaptively in order to reduce the required computational effort and enhance its performance. In the last part of the paper, four numeric examples are presented to demonstrate the performance of the proposed algorithm. Results show that the proposed algorithm has great ability in producing a set of solutions which cover all parts of the true Pareto front.     

An Implicit Enumeration Algorithm for the Nonpreemptive Shop Scheduling Problem

The purpose of this paper is to report on the development and computational results of an implicit enumeration algorithm for the nonpreemptive shop scheduling problem. This algorithm is inspired by the disjunctive graph representation of the problem and is somewhat similar to the branch-and-bound approach. In order to guide the search, the algorithm employs a decision vector which is designed to reduce the number of iterations. Attention has been focused on improving the quality of the initial solution with minimum computational effort involved. The rapid convergence of the algorithm is demonstrated by solving problems with up to 1000 operations. The results obtained are compared favorably with a number of published procedures. Generalizations of the algorithm are also provided.     

Multiresolution local contrast enhancement of x-ray images for poultry meat inspection

A multiresolution-analysis-based local contrast transform is proposed to enhance local structures in x-ray images. The local contrast is defined as a ratio of the local intensity variation to the local mean. With wavelet multiresolution decomposition, the detail coefficients and approximation coefficients are interpreted, respectively, as local variations and local averages in virtue of the localization property of wavelet transform. Based on the local contrast transform, an algorithm is developed to modify coefficients before wavelet synthesis. An across-scale local contrast is obtained when the scale associated with the local variation is different from that of the local mean. The nonlinearity and local adaptiveness properties of local contrast transform result in structural enhancement in local dark regions in the reconstructed images. We applied this technique to deboned poultry inspection using x-ray images. Because of its high x-ray absorption, a foreign inclusion appears as a low-intensity object in an x-ray image, thus resulting in contrast enhancement in the reconstructed multiresolution images.     

基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。     

面向批量订单包装的物流箱规格优化问题研究

目的 针对面向仓储物流环境下多型号多批量产品的订单包装问题,提出一种预制物流箱规格优化模型及算法。方法 对产品订单建立订单分包规则,确定分包方案,以订单包装材料总成本最小为优化目标建立物流箱规格优化模型。针对该模型提出一种改进模拟退火算法,通过贪婪策略求解最优分包方案,降低模型计算复杂度,设计一种新型解更新算子,以提高算法寻优能力,设计一种自适应步长策略,以平衡算法前期全局搜索与后期局部搜索的能力。结果 通过实例证明,文中提出的算法相较于其他算法,具有更强的求解能力,与实例企业仓储包装现状相比,同批订单降低了17%的包装材料成本。结论 该方法可用于解决产品种类多、尺寸差异大、动态更新等应用场景下的系列运输包装纸箱规格优化问题,为企业物流运输管理提供了一种有效的包装优化思路和解决方法。