Modified Cuckoo Search Algorithm to Solve Economic Power Dispatch Optimization Problems

A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.

Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

Constrained optimization problems are very important as they are encountered in many science and engineering applications.As a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization problems.This algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search method.Simulation and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.

Evaluation and intelligent deployment of coal and coalbed methane coupling coordinated exploitation based on Bayesian network and cuckoo search

Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future.

Control allocation for aircraft with input constraints based on improved cuckoo search algorithm

The control allocation problem of aircraft whose control inputs contain integer constraints is investigated. The control allocation problem is described as an integer programming problem and solved by the cuckoo search algorithm. In order to enhance the search capability of the cuckoo search algorithm, the adaptive detection probability and amplification factor are designed. Finally, the control allocation method based on the proposed improved cuckoo search algorithm is applied to the tracking control problem of the innovative control effector aircraft. The comparative simulation results demonstrate the superiority and effectiveness of the proposed improved cuckoo search algorithm in control allocation of aircraft.

A Method for Crude Oil Selection and Blending Optimization Based on Improved Cuckoo Search Algorithm

Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.

Parameter Estimation of Mixed Weibull Distributions Using Cuckoo Search

The lifetime data of products with multiple failure modes which are collected from life testing are often fitted by the mixed Weibull distributions. Since the mixed Weibull distributions contain no less than five parameters,the parameter estimation is difficult and inaccurate. In order to enhance the accuracy,a new method of parameter estimation based on Cuckoo search( CS) is proposed. An optimization model for the mixed Weibull distribution is formulated by minimizing the residual sum of squares. The optimal parameters are searched via CS algorithm. In the case study,the lifetime data come from the life testing of diesel injectors and are fitted by the twocomponent Weibull mixture. Regarding the maximum absolute error and the accumulative absolute error between estimated and observed values as the accuracy index of parameter estimation,the results of four parameter estimation methods that the graphic estimation method,the nonlinear least square method,the optimization method based on particle swarm optimization( PSO) and the proposed method are compared. The result shows that the proposed method is more efficient and more accurate than the other three methods.

Convergence Analysis of Cuckoo Search by Creating Markov Chain

Cuckoo search(CS) has been used successfully for solving global optimization problems.From a theoretical point of view,the convergence of the CS is an important issue.In this paper,convergence analysis of CS was studied.The transition probability characteristics of the population to construct a Markov chain were analyzed.The homogeneity of the Markov chain was derived based on stochastic process theory.Then it was proved to be an absorbing state Markov chain.Consequently,the global convergence of CS was deduced based on conditions of convergence sequence and total probability formula,and the expected convergence time was given.Finally,a series of experiments were conducted.Experimental results were analyzed and it is observed that CS seems to perform better than PSO.

A Cuckoo Search Detector Generation-based Negative Selection Algorithm

The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from nonself.It asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities.However,these detectors have limited performance.Redundant detectors are generated,leading to difficulties for detectors to effectively occupy the non-self space.To alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the NSA.Inbuilt characteristics such as mutation,crossover,and selection operators make the CS attain global convergence.With the use of Lévy flight and a distance measure,efficient detectors are produced.Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested datasets.The proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,respectively.Thus,the generation of highest detection rates and lowest false alarm rates can be achieved.

Cuckoo search algorithm-based optimal deployment method of heterogeneous multistatic radar for barrier coverage

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

基于IMOCS-BP神经网络的锂离子电池SOH估计

锂离子电池随着循环充放电次数的增长,其健康状态SOH(state-of-health)会随之发生一定程度的衰减。针对以上问题,设计了一种基于改进的多目标布谷鸟搜索IMOCS(improved multi-objective Cuckoo search)-BP神经网络的锂离子电池健康状态估计方法,在避免算法陷入局部最优的同时自适应改变布谷鸟搜索CS(Cuckoo search)算法更新概率和搜索步长,解决CS算法收敛速度慢和求解精度低的问题。以IMOCS算法和BP神经网络结合,对节点空间范围进行全局搜索,降低权值和阈值的初值对BP神经网络的影响,实现参数优化。通过Matlab仿真,验证了基于IMOCS-BP神经网络的SOH估计算法误差低、性能强,实现了锂电池SOH的精准预测。

基于CNN-LSTM-CS工业管道腐蚀率预测模型

针对传统工业管道腐蚀率预测模型存在特征提取依赖人工经验和泛化能力不足的问题,本文将卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory,LSTM)相结合,提出了基于布谷鸟优化算法(cuckoo search,CS)的CNN-LSTM-CS网络模型,实现对工业管道腐蚀率预测.首先,对采集的管道腐蚀数据集进行归一化预处理;然后,利用CNN网络提取影响管道腐蚀率因素的深层次特征信息,并通过训练LSTM网络构建CNN-LSTM预测模型;最后,采用CS算法对预测模型进行参数优化,减少预测误差,实现腐蚀率的精准预测.实验结果表明,对比几种典型的腐蚀率预测方法,本文提出的方法具有更高的预测精度,为工业管道腐蚀率检测提供新的思路.

Adaptive Uniform Circular Array Synthesis Using Cuckoo Search Algorithm

Naturally suited array geometry for 360° coverage is the uniform circular array (UCA). A comparison of two types of uniform circular array configurations is presented in this paper. Due to its symmetrical geometry UCA is always targeted which results in minimal change inside lobe levels and beam width when scanned by a phased array antenna. Particle Swarm Optimization and Cuckoo algorithm are used for the calculation of complex weights of the array elements. Comparisons are drawn in the context of adaptive beam forming capabilities. Obtained results suggest that planar uniform circular array (9:10) using Cuckoo algorithm, has better beam forming properties with also reduced side lobe levels when compared to other geometry.

Parallelizing Modified Cuckoo Search on MapReduce Architecture

Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search （MRMCS）, an efficient modified cuckoo search （MCS） implementation on a MapReduce architecture--Hadoop. MapReduce particle swarm optimization （MRPSO） from a previous work is also implemented for comparison. Four evaluation functions and two engineering design problems are used to conduct experiments. As a result, MRMCS shows better convergence in obtaining optimality than MRPSO with two to four times speed-up.

Cuckoo Search for Solving Economic Dispatch Load Problem

Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.

基于IMOCS算法的跨流域水资源多目标优化调配

为有效求解跨流域水资源多目标优化调配问题,提出一种改进多目标布谷鸟算法(improved multi-objective cuckoo search algorithm,IMOCS)。针对多目标布谷鸟优化算法(multi-objective cuckoo search algorithm,MOCS)收敛速度慢、容易陷入局部最优解的缺点,引入混沌理论和变异机制,采用自适应发现概率和步长改进MOCS,形成IMOCS算法。以南水北调东线工程江苏段为例,构建跨流域水资源多目标调配模型,分别采用IMOCS和MOCS求解模型,并运用基于组合赋权的非负矩阵分解法对2种算法所得的Pareto解集进行评价。结果表明:IMOCS在收敛性、多样性和综合性能方面优于MOCS,能够得到更高质量的Pareto解集;相较于50%、75%和95%来水频率下的MOCS所求解的最优配置方案,IMOCS所求解的最优配置方案缺水总量减少0.21亿、0.51亿和0.07亿m~3,损失水量分别减少了0.13亿、1.53亿和1.11亿m~3。因此,IMOCS可为跨流域水资源多目标优化配置计算提供有效的算法参考。

基于Attention-CS-LSTM乙烯裂解炉管温度预测

在乙烯生产过程中,针对乙烯裂解炉管温度难监测的情况,需要对传统的温度测量方法进行改进,通过数据模型下的优化操作可以有效预测乙烯裂解炉出口温度,当出现温度波动时进行干预,提高产品效率和生产安全。文章将改进的布谷鸟算法优化LSTM(CS-LSTM)应用于真实工业数据,并与四种模型进行比较。仿真结果表明,采用Attention-CS-LSTM预测准确率明显提高,且具有良好的稳态准确度,该方法的温度预测准确率为95%。

基于CS算法的Markov模型及收敛性分析

为完善布谷鸟搜索(CS)算法的收敛性理论,建立CS算法的Markov链模型,分析该Markov链的有限齐次性,在此基础上通过分析鸟窝位置的群体状态转移过程,指出随机序列将进入最优状态集,同时证明CS算法满足随机搜索算法全局收敛的2个条件。通过仿真实验验证CS算法可收敛于全局最优,从而确保CS算法的全局收敛性。

一种改进的CS算法及其在微电网优化中的应用

为解决布谷鸟搜索算法存在的后期收敛速度慢,求解精度低以及容易陷入局部最优点等问题,提出了一种改进的布谷鸟搜索算法:CS-EO搜索算法。在该搜索算法中,通过将布谷鸟算法收敛速度快和全局搜索的优点与极值动力学优化算法强大的局部搜索能力进行有机的结合,在保证布谷鸟算法求解速度的前提下,提高了布谷鸟算法的求解精度。函数寻优测试的仿真结果表明改进的布谷鸟搜索算法相较于布谷鸟搜索算法以及粒子群算法都具有更好的寻优性能。最后将此算法应用于微电网的负荷优化调度中,取得了较为令人满意的结果。

混合CS算法的DE算法

为解决基本差分进化算法的缺陷,利用布谷鸟搜索(CS)算法寻优能力强的优点,在DE每次完成选择操作后,不直接进入下一次迭代,而是引入CS算法,继续进行搜索,这样就增加了粒子的搜索活力,从而得到一种新的差分进化算法。经过对6个标准测试函数的大量实验计算表明,该算法能有效克服DE算法的缺陷,使寻优精度有较大改进。将算法应用于求解非线性方程组问题,给出了数值算例。