基于改进布谷鸟搜索算法的图像边缘检测

针对传统边缘检测算法自适应能力差、固定阈值、背景噪声抑制的问题, 为了获得更理想的图像边缘检测结果, 提出了一种基于改进布谷鸟搜索算法的图像边缘测算法. 首先通过灰度图像矩阵的一阶导数得到灰度图像的梯度值矩阵, 然后用改进布谷鸟搜索算法根据布谷鸟繁殖行为找到搜索图像的梯度最大值, 检测出图像的边缘, 最后采用仿真实验对算法的性能进行检测. 仿真实验结果表明, 本文算法能快速、准确地检测出图像的边缘, 且优于其他传统边缘检测算法.     

Shape optimization of skeleton structures using mixed-discrete variables

The shape and size optimization problem of a structural body is solved by a mixed-discrete algorithm, where a continuous variable is handled as a pseudo-continuous one. It is found that handling a continuous variable in the pseudo-continuous sense can reduce analytical difficulties. The mixed-discrete algorithm uses two different techniques of which one is the gradient based steepest descent technique and the other is the gradient free Rosenbrock orthogonalization procedure. Both techniques are modified to suit discrete or pseudo-continuous variables. Hybridization of two different types of optimization techniques enables the algorithm to solve different kinds of optimization problems.     

Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks

Developing a precise dynamic model is a critical step in the design and analysis of the overhead crane system. To achieve this objective, we present a novel radial basis function neural network (RBF-NN) modeling method. One challenge for the RBF-NN modeling method is how to determine the RBF-NN parameters reasonably. Although gradient method is widely used to optimize the parameters, it may converge slowly and may not achieve the optimal purpose. Therefore, we propose the cuckoo search algorithm with membrane communication mechanism (mCS) to optimize RBF-NN parameters. In mCS, the membrane communication mechanism is employed to maintain the population diversity and a chaotic local search strategy is adopted to improve the search accuracy. The performance of mCS is confirmed with some benchmark functions. And the analyses on the effect of the communication set size are carried out. Then the mCS is applied to optimize the RBF-NN models for modeling the overhead crane system. The experimental results demonstrate the efficiency and effectiveness of mCS through comparing with that of the standard cuckoo search algorithm (CS) and the gradient method.     

Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation

One of most important challenges in Unmanned (Combat) Aerial Vehicles (UCAV) is improvement of survivability and that can be achieved by well designed aerodynamic and Radar Cross Section (RCS) shapes. The aerodynamic efficiency aims to providing a short distance take-off, long endurance and better maneuverability. In addition, the stealth property is one of the essential requirements to complete diverse missions and ensure the survivability of UAVs. This paper explores the application of a robust Evolutionary Algorithm (EA) for aerofoil sections and wing plan form shape design and optimisation for the improvement of aerodynamic performance and the reduction of Radar Cross Section. The method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Results obtained from the optimisation show that utilising the designing transonic wing aerofoil sections and plan form in combination with evolutionary techniques improve the aerodynamic efficiency. It is shown that this optimisation procedure produced a set of shock-free aerofoils and achieved supercritical aero-diamond wings. Results also indicate that the method is efficient and produces optimal and Pareto non-dominated solutions.     

带外部存档的正交交叉布谷鸟搜索算法

布谷鸟搜索算法是一种新兴的仿生优化技术,其迭代使用Lévy flights随机走动和Biased随机走动搜索新的个体.在Biased随机走动中,随机交叉搜索方式具有一定的盲目或无效率,这将可能削弱布谷鸟搜索算法的搜索能力.为了改善布谷鸟搜索算法的搜索能力,提出带外部存档的正交交叉布谷鸟搜索算法(orthogonal crossover cuckoo search algorithm with external archive, OXCS).正交交叉被嵌入于Biased随机走动中以提高交叉搜索的效率.外部存档维护一定时期内的种群历史信息,并为正交交叉操作提供一个父本.实验结果说明提出的策略能够有效地改善布谷鸟搜索算法的搜索能力,并提高求解连续函数优化问题的收敛速度和解的质量.     

Seeker optimisation (human group optimisation) algorithm with chaos

Seeker optimisation algorithm (SOA), also referred to as human group metaheuristic optimisation algorithms form a very hot area of research, is an emerging population-based and gradient-free optimisation tool. It is inspired by searching behaviour of human beings in finding an optimal solution. The principal shortcoming of SOA is that it is easily trapped in local optima and consequently fails to achieve near-global solutions in complex optimisation problems. In an attempt to relieve this problem, in this article, chaos-based strategies are embedded into SOA. Five various chaotic-based SOA strategies with four different chaotic map functions are examined and the best strategy is chosen as the suitable chaotic scheme for SOA. The results of applying the proposed chaotic SOA to miscellaneous benchmark functions confirm that it provides accurate solutions. It surpasses basic SOA, genetic algorithm, gravitational search algorithm variant, cuckoo search optimisation algorithm, firefly swarm optimisation and harmony search the proposed chaos-based SOA is expected successfully solve complex engineering optimisation problems.     

Discrete cuckoo search algorithm for the travelling salesman problem

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics.     

Applying modified cuckoo search algorithm for solving systems of nonlinear equations

Neural Computing and Applications - In the present paper, a modified cuckoo search algorithm is proposed for solving nonlinear equations, that is, the niche cuckoo search algorithm (NCSA) based on...     

A comprehensive study of cuckoo-inspired algorithms

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them.

     

参数动态调整的自适应布谷鸟算法

为提高布谷鸟算法的收敛速度和求解精度,提出了一种基于自适应机制的改进布谷鸟算法。该算法在迭代初期和末期分别使用两种自适应策略来动态调整步长和发现概率,提高了算法的局部和全局寻优能力。利用10个标准测试函数对基本布谷鸟算法、所提出的改进算法以及其他智能优化方法进行了仿真对比验证,结果表明所提出的改进布谷鸟算法在求解精度、稳定性以及收敛速度上都具有一定优势。     

A generalized “max-min” sample for surrogate update

The preliminary Multidisciplinary Design and Optimisation of a flexible wing aerofoil apropos a small Unmanned Air Vehicle is performed using a multifidelity model-based strategy. Both the passively adaptive structure and the shape of the flexible wing aerofoil are optimised for best aerodynamic performance under aero-structural constraints, within a coupled aeroelastic formulation. A typical flight mission for surveillance purposes is considered and includes the potential occurrence of wind gusts. A metamodel for the high-fidelity objective function and each of the constraints is built, based on a tuned low-fidelity one, in order to improve the efficiency of the optimisation process. Both metamodels are based on solutions of the aeroelastic equations for a flexible aerofoil but employ different levels of complexity and computational cost for modelling aerodynamics and structural dynamics within a modal approach. The high-fidelity model employs nonlinear Computational Fluid Dynamics coupled with a full set of structural modes, whereas the low-fidelity one employs linear thin aerofoil theory coupled with a reduced set of structural modes. The low-fidelity responses are then corrected according to few high-fidelity responses, as prescribed by an appropriate Design of Experiment, by means of a suitable tuning technique. A standard Genetic Algorithm is hence utilised to find the global optimum, showing that a flexible aerofoil is characterised by higher aerodynamic efficiency than its rigid counterpart. Wing weight reduction is also accomplished when a Multiobjective Genetic Algorithm is adopted.     

量子布谷鸟协同搜索的垃圾回收路径规划方法

针对城市垃圾回收路径规划问题,提出了一种量子布谷鸟协同搜索算法,用于优化最短路径.首先,采用Bloch球面坐标量子编码来扩大解空间;然后设计了一种基于差分进化的量子布谷鸟搜索策略,实现较差个体的改进以及劣势个体与优势个体之间的信息交换,增强全局搜索能力;最后,利用一种局部邻域搜索算法进一步提高解的质量.理论分析了所提算法的收敛性.基于无线传感网络采集数据进行了仿真实验,将量子布谷鸟协同搜索算法与传统遗传算法和量子布谷鸟搜索算法分别比较,求解垃圾回收最短路径问题的最优解和平均解均改进了20%~40%,结果证明了量子布谷鸟协同搜索算法的优越性.     

Face detection approach from video with the aid of KPCM and improved neural network classifier

In recent years, the detection of a human face from the video has become an interesting research topic due to the video surveillance and other security issues. Efficient face detection from the video has become an immense need as it can provide various identity measures in the field of defense and other security-related areas. In our proposed method we have developed an efficient method of face detection to index a particular face from different video shots. The proposed method can be divided into Different modules. In the first module, human face from the video is extracted using segmentation technique. In our proposed method, we have used Kernel-based Possibilistic C-Means for segmentation purpose. The second module in our method is the feature extraction process where shape, LBP, and some geometrical features are extracted. The various shape features like area, circularity, and eccentricity are extracted. Once the feature values are extracted we track the particular face using forward tracking process. After the tracking process, we employ the classification technique. The classifier we utilized here is the improved neural network where the weights factors are optimized using the modified cuckoo search algorithm. The performance is compared with some existing works in order to prove the efficiency of our proposed method.     

量子衍生布谷鸟算法及在地层对比中的应用

为提高布谷鸟搜索算法的寻优能力,通过在经典布谷鸟搜索算法中引入量子计算机制,提出一种量子衍生布谷鸟搜索算法。该算法采用量子比特编码个体,采用泡利矩阵确定旋转轴,采用Levy飞行原理确定旋转角度,采用量子比特在Bloch球面上的绕轴旋转实现个体更新。针对钻井剖面地层对比的具体特点及需要满足的约束条件,提出应用量子衍生布谷鸟算法进行地层对比优化的实施方案,该方法既能对比不同地层之间的相似性,也能处理对比井地层因断层或尖灭等因素造成的缺失。实验结果表明,在复杂地质情况下,该算法是有效的和可行的。      

量子衍生布谷鸟搜索算法

为提高布谷鸟搜索算法的寻优能力,通过在经典布谷鸟搜索算法中引入量子计算机制,提出了一种量子衍生布谷鸟搜索算法.该算法采用量子比特编码个体,采用泡利矩阵确定旋转轴,采用Levy飞行原理确定旋转角度,采用量子比特在Bloch球面上的绕轴旋转实现个体更新.标准函数极值优化的实验结果表明,与传统布谷鸟搜索算法相比,该算法的搜索能力确有明显提升.     

Power law-based local search in spider monkey optimisation for lower order system modelling

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO (PLSMO). The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.     

Cuckoo search: recent advances and applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.     

基于改进布谷鸟搜索的k-means算法的离群点检测

为了解决k-means算法的离群点检测容易受到初始聚类中心的影响陷入局部最优的问题,本文提出一种基于改进布谷鸟搜索的k-means算法的离群点检测方法.首先,对原始布谷鸟搜索算法中的发现概率和莱维飞行步长做自适应策略改进并进行实验仿真;其次讨论改进后的布谷鸟搜索算法的收敛性问题;最后将改进后的布谷鸟搜索算法与k-mea...     

Die shape optimisation for net-shape accuracy in metal forming using direct search and localised response surface methods

In this paper, three direct search algorithms, i.e. a modified simplex, random direction search and enhanced Powell’s methods together with a new localised response surface method are presented and applied to solve die shape optimisation problems for achieving net-shape accuracy in metal forming processes. The main motivation is to develop efficient and easy to implement optimisation algorithms in metal forming simulations which often involve complex tool and workpiece interaction and coupled thermal and mechanical analysis. Three case studies are presented including a simple upsetting, a 2D blade forging and a forward extrusion problem. In all cases, the objective was to achieve net-shape accuracy of the formed parts, one important criterion for precision forming. C+ + programs were developed to implement these algorithms and to automatically integrate optimisation computation and forging simulation. The optimisation results from the three case problems show that direct search based methods especially the modified simplex and the localised response surface methods are computationally efficient and robust for net-shape forging and extrusion optimisation problems. It is also suggested that these methods can be used in more complex forging problems where die shape design and optimisation are essential for achieving net-shape accuracy.