Adaptive Cuckoo Search Research Articles

UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

Abstract Addressing the optimal path planning problem encountered by swarm of Unmanned Aerial Vehicle (UAV) in three-dimensional space under multiple constraints, the Multi-population Adaptive Cuckoo Search and Grey Wolf Optimizer (MACSGWO) integrates Multi-Population (MP) strategies and adaptive evolutionary optimizer including the enhanced Adaptive Grey Wolf Optimizer (AGWO) and Adaptive Cuckoo Search (ACS).
The optimizer strategically divides the initial population into multiple sub-groups, enabling each sub-group to independently iterate. 
During the iteration process, the algorithm adaptively adjusts parameters based on the optimal fitness values obtained by each sub-group after each iteration. The iteration cycle is divided into two stages: during the global exploration phase, each sub-group autonomously executes AGWO and periodically shares the fitness information of the Alpha wolf with other sub-groups, accelerating convergence.
In the subsequent local optimization phase, MACSGWO dynamically decides whether to initiate ACS based on the disparity in the best fitness of each sub-group after each iteration, assisting the algorithm in escaping local optima. 
In experiments involving various complex benchmark functions and swarm path planning scenarios, MACSGWO demonstrated significant superiority in solution stability, convergence speed, and optimal convergence value compared to multiple existing variant algorithms. The integration of MACSGWO with the best relay UAV selection strategy further optimized the communication efficiency within the swarm. MACSGWO ensures the efficient resolution of UAV swarm path planning problems, providing robust support for optimization challenges in complex, multi-constraint scenarios.

An adaptive operator selection cuckoo search for parameter extraction of photovoltaic models

Accurate, reliable, and efficient extraction of photovoltaic (PV) model parameters is an essential step towards PV system simulation, control, and optimization. Nevertheless, this problem is still facing great challenges because of its intrinsic nonlinear, multivariate, and multimodal properties. In this paper, a new variant of cuckoo search (CS), adaptive operator selection CS (AOSCS), is advanced for the PV model parameter extraction problems. AOSCS includes two major improvements: (1) an adaptive operator selection mechanism is developed to automatically assign the workloads of exploration and exploitation operators, and (2) the exploration and exploitation operators used in the original CS are modified to promote the exploration capability and reduce the blindness of search, respectively. The performance of AOSCS is firstly validated on CEC 2017 test suite and then it is utilized to solve the parameter extraction problems of five PV models. Moreover, further experiments on two commercial PV modules under distinct irradiance and temperature levels are also conducted to evaluate the practicality of the proposed algorithm. It is manifested that the results yielded by AOSCS are very competitive relative to other parameter extraction approaches. Accordingly, the proposed AOSCS is able to be served as an up-and-coming candidate algorithm for PV model parameter extraction problems.

Adaptive cuckoo search algorithm based fuzzy C means clustering with random walker algorithm for liver segmentation using CT images

Adaptive cuckoo search algorithm based fuzzy C means clustering with random walker algorithm for liver segmentation using CT images

A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation.

Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

Abstract This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.

Numerical design optimization of real size complex steel space frame structures using a novel adaptive cuckoo search method

AbstractIn this study, real size complex steel space frame structures are numerically designed to achieve optimal design weight. For this aim, standard cuckoo search algorithm is rectified through a newly proposed adaptive method over two fundamental algorithmic parameters of alien egg probability detection (Pa) and step size control (α) to overcome incompetency of trapping into local optima. Besides, to strengthen exploitation phase of newly proposed algorithm, it is boosted with greedy selection (GS). So, novel algorithm has more promising exploration and exploitation capabilities. An 8‐story, 1024‐member and a 20‐story, 1860‐member real size complex steel space frame structures are selected as design examples. Also, initially to verify the supremacy of novel algorithm, a well‐known welded beam is optimized as a benchmark structural design problem. Afterwards, the steel space frame structures are optimally designed via novel algorithm. Since ready steel section lists are utilized as selection pool for design variables, discrete programming problem is come into existence. The dead, live, snow, and wind design loads acting on frame structures are calculated in direction of ASCE 7‐05 provisions. Furthermore, the structural design constraints are determined from LRFD‐AISC specifications. Eventually, the newly proposed adaptive cuckoo search algorithm boosted with GS presents outstanding algorithmic performance.

Design and performance analysis of hybrid optimization MPPT controller for proton exchange membrane fuel cell system with DC-DC converter

At present days, the hybrid Electric Vehicle (EV) utilizing the fuel stack power generation network. Most of the fuel cell output characteristics are nonlinear fashion. Also, its operating point fluctuates based on the input temperature of the fuel stack. So, the receiving of peak voltage from the fuel source is less. To enhance the voltage delivering capability of the fuel stack, an adaptive Cuckoo Search Controller (CSC) based fuzzy logic method is included in the Maximum Power Point Tracking (MPPT) block. The proposed controller is compared with the other basic hybrid controllers which are Perturb & Observe based Artificial Neural Network (P&O with ANN) controller, plus Hill Climb dependent ANN controller (HC with ANN). The major features of proposed power point finding controller are few steady state distortions, few voltage ripples, high tracking convergence speed, plus less iterations required for catching the MPP position. The fuel stack generation voltage is less. In order to increase the voltage of the proposed network, the boost converter is applied. The entire system is studied by the use of MATLAB/Simulink software.

Fabric defect detection using ACS-based thresholding and GA-based optimal Gabor filter

Fabric defect detection is closely related to quality control. Therefore, it is an important topic in the modern textile field. In this article, a new fabric defect detection method using ACS-based thresholding and GA-based optimal Gabor filter is proposed. The multi-level gray-scale image thresholding is improved by using Adaptive cuckoo search (ACS) to calculate the best threshold. In order to suppress texture interference, the optimal Gabor filter calculated by genetic algorithm (GA) is used to achieve the optimal feature extraction of fabric defects. A specific image acquisition system is set up to obtain clear defective fabric images and corresponding defect-free images which are used to build the database. The performance of the method is evaluated by extensive experiments on various types of fabric defects. Experimental results show that the proposed method, effective and robust, is superior than the other six methods.

Combined forecasting tool for renewable energy management in sustainable supply chains

Effective managing sustainable supply chain is on the most cutting-edge position of various organizations in the process of delivering products. There are many optimization technologies that have been applied in sustainable supply chain management, aiming to decrease the carbon emission level of production process. Among these methods, designing reliable renewable energy forecasting approaches is conductive to power management and optimization in sustainable and circular supply chain. However, irregular and non-stationary fluctuations of wind speed is a major obstacle to optimize the applications of renewable energy in sustainable supply chain management. There are various approaches for wind speed prediction, yet most of them ignore the significance of hyperparametric selection and combined forecasting strategy, resulting in unsatisfactory prediction results. To remedy the drawbacks, a combined prediction framework is proposed, which uses grid search to select suited hyperparameters of sub-models and employs an improved intelligent optimization algorithm (ranking-based adaptive cuckoo search algorithm) to calculate the optimal weighting coefficient of sub-models. Nine datasets are collected to validate the proposed model and 15 benchmark models. The simulations revel that the proposed model yields the satisfactory prediction level, which precedes comparative models based on statistical test results. Hence, it is a valuable tool for decision makers to provide key reference information in sustainable supply chain management and optimization.

Development of two stage optimization-based demand response technique for smart homes under real time pricing

Residential load management deals with two major objectives viz. minimizing the cost of monthly electricity bill and peak demand of power consumption. Both objectives can be achieved by effective operational scheduling of smart home appliances. These two objectives are conflicting in nature because rescheduling of appliances in order to minimize one objective may result in the rise of another. To achieve both objectives concurrently, an algorithm is suggested in this paper based on artificial intelligent techniques like cuckoo search, hybrid GA-PSO, and adaptive cuckoo search. The proposed algorithm is tested successfully on seven households of different monthly power consumption and real data of dynamic pricing options for electricity applicable in two utilities. To reduce the risk associated with real-time price, a cost function based on inclining block rates (IBR) is also suggested for both the utilities. A novel approach to find the threshold limit of hourly power consumption is also suggested in this paper. The proposed algorithm solves the optimization problem in two stages and validates its performance by successfully achieving both objectives simultaneously.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms by using the CEC-2013 test function. The results show that PACS algorithm outperforms other algorithms in 20 of 28 test functions. Due to the superior performance of PACS algorithm, this paper uses it to solve the problem of the rectangular layout. Experimental results show that this scheme has a significant effect, and the material utilization rate is improved from 89.5% to 97.8% after optimization.

Pumping machine fault diagnosis based on fused RDC-RBF.

At present, the fault diagnosis of pumping units in major oil fields in China is time-consuming and inefficient, and there is no universal problem for high requirements of hardware resources. In this study, a fault fusion diagnosis method of pumping unit based on improved Fourier descriptor (IDF) and rapid density clustering RBF (RDC-RBF) neural network is proposed. Firstly, the minimum inertia axis of the center of gravity of the indicator diagram is obtained. The farthest point of the intersection of the inertial axis and the contour is determined as the starting point. Then Fourier transform is performed on the contour boundary of the graph to obtain the feature vector. Then, combining with the idea of fast density clustering algorithm, the number of hidden layer neurons of RBF is determined by finding the point with the highest density and using it as the hidden layer neuron. At the same time, the characteristics of Gaussian function are introduced to ensure the activity of hidden layer neurons. Finally, through dynamic adaptive cuckoo search (DACS), the step size is automatically adjusted according to the convergence speed of the objective function of RBF, and the efficiency and accuracy of RBF in different search stages are balanced. The optimal parameters such as the width and weight of RBF are determined, and the optimal RDC-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping units, and compared with the current mainstream models. The average detection accuracy of the fusion RDC-RBF fault diagnosis method proposed in this paper reaches 96.3%. The measured results have high accuracy and short time. At the same time, this method is currently applied to oil production sites such as Shengli Oilfield in China, which greatly reduces the human resources required for fault diagnosis of pumping units in the past.

An Evolutionary Dynamic Control Cuckoo Search Algorithm for Solving the Constrained Engineering Design Problems

The key contribution of this work is the dynamic control cuckoo search (DCCS) method. Nonetheless, the adaptive cuckoo search (ACS) appears to be effective in utilizing the exploitation and exploration by using the best solution followed by an adaptive step size to determine the next-generation solutions. However, its convergence rate is limited. To solve this problem, the authors use dynamic control, adaptive step size, and randomization in the cuckoo search path for the following generations. A better tradeoff between exploitation and exploration is achieved, allowing for a faster convergence rate. The 23 traditional and 10 CEC2019 benchmark functions are used for validations. When the DCCS results are compared to the well-known methods using scalability and statistical tests like Wilcoxon's rank-sum test, it shows a significant improvement. Friedman's mean rank test is also ranked the strategic DCCS top. Furthermore, constrained engineering design problems 1) welded beam design and 2) pressure vessel design are solved. The DCCS would be useful for optimization.

A novel multiple training-scale dynamic adaptive cuckoo search optimized long short-term memory neural network and multi-dimensional health indicators acquisition strategy for whole life cycle health evaluation of lithium-ion batteries

State of health evaluation of lithium-ion batteries has become a significant research direction in related fields attributed to the crucial impact on the reliability and safety of electric vehicles. In this research, a dynamic adaptive cuckoo search optimized long short-term memory neural network algorithm is proposed. The aging mechanism of the battery is described effectively by extracting and selecting high correlation health indicators including voltage, current, charging time, etc. A dynamic adaptive strategy is introduced to the cuckoo search algorithm to stabilize the step size and improve the global search ability. The hyperparameter optimization and noise filtering problems of the long short-term memory model are solved and the accuracy of the algorithm is improved by taking advantage of the established dynamic adaptive cuckoo search algorithm. The accuracy and effectiveness of the proposed method are verified based on the seven groups of battery aging datasets from the National Aeronautics and Space Administration and the University of Maryland. Compared with the long short-term memory and convolutional neural network long short-term memory, the mean absolute error of the results obtained by the proposed algorithm is kept under 2%, the root mean square error is less than 3%, and the average absolute percentage error is less than 3%. The results indicate the algorithm has better fitting performance, stronger robustness, and generality.

Novel Square Error Minimization-Based Multilevel Thresholding Method for COVID-19 X-Ray Image Analysis Using Fast Cuckoo Search

Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ray images. Most of the threshold selection techniques are entropy-based. Nevertheless, these techniques suffer from their dependencies on the spatial distribution of grey values. To tackle these issues, a novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field. The firsthand Square Error (SE)-based objective function is suggested. The second key contribution is the new optimizer called Fast Cuckoo Search (FCS), which is useful and brings novel ideas into the subject, used to optimize the suggested objective functions for computing the optimal thresholds. To ensure a faster convergence with a quality optimal solution, we include extra exploitation together with a chance factor. The FCS is validated using the well-known classical and CEC 2014 benchmark test functions, which shows a significant improvement over its predecessors—Adaptive Cuckoo Search (ACS) and other state-of-the-art optimizers. Further, the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed. To experiment, images are considered from the Kaggle Radiography database. We have compared its performances with Tsallis, Kapur’s, and Masi entropy-based techniques using well-known segmentation metrics and achieved a performance increase of 2.95%, 5.51% and 10.50%, respectively. The proposed method shows superiority using Friedman’s mean rank statistical test and ranked first.

Secure Routing in MANETS Using Adaptive Cuckoo Search and Entropy Based Signature Authentication

Secure Routing in MANETS Using Adaptive Cuckoo Search and Entropy Based Signature Authentication

Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication

PurposeThe digital media recording and broadcasting classroom using Internet real-time intelligent image positioning and opinion monitoring in communication teaching is researched and analyzed.Design/methodology/approachFirst, spatial grid positioning and monitoring and image intelligent recognition technologies were used to extract and analyze teaching images by mastering Internet of Things (IoT) technology and establishing an intelligent image positioning and opinion monitoring digital media recording and broadcasting system framework. Next, a positioning node algorithm was utilized to measure the image distance, and then a moving node location model under the IoT was established. In addition, a radial basis function (RBF) neural network was used to realize the signal transmission function. The experimental data of the adopted RBF based on the optimization of the adaptive cuckoo search (ACS-RBF) neural network, particle swarm algorithm neural network, and method of least squares optimization were compared and analyzed. In addition, a more efficient RBF neural network was adopted. Finally, the digital media recording and broadcasting classroom scheme of real-time intelligent image positioning and opinion monitoring was designed. In addition, the application environment of digital media actual teacher teaching was detected, and recording and broadcasting pictures were analyzed and researched.FindingsThe actual value, predicted value, and the number of predicted samples of the ACS-RBF model were all better than those of the two other neural networks. According to the analysis and comparison of the sampling optimization Monte Carlo localization (SOMCL), Monte Carlo, and genetic algorithm optimization-based Monte Carlo positioning algorithms, the SOMCL algorithm showed better robustness, and its positioning efficiency was superior to that of the two other algorithms. In addition, the SOMCL algorithm greatly reduced the positioning and monitoring energy consumption.Originality/valueThe application of real-time intelligent image positioning and monitoring technology in actual communication teaching was realized in the study.

Dynamic Economic Dispatch With CHP and Wind Power Considering Different Time Scales

The integration of large-scale renewables into the multienergy system has brought a series of problems and challenges to the optimal dispatch of system, especially how to effectively realize the dispatch and accommodation of wind power. In this article, a dynamic economic dispatch (DED) model of wind/combined heat and power (CHP) hybrid energy system considering uncertainty of multiple wind farms and different dispatch time scales is proposed, which aims to minimize expected total energy generation cost based on the typical scenarios of upper bound of wind farm output. The spatial dependence of hourly output of multiwind farm is modeled by copula-based model and improved scenario reduction technique, and the different adjustment time scales of heating system and power system are modeled as well to achieve more reasonable and accurate system dispatching. In addition, an improved adaptive cuckoo search algorithm (ACSA) is proposed for the solution of the DED model of wind/CHP hybrid energy system with highly nonlinear constraints, and a shared parallel computing platform is employed to realize the acceleration of the ACSA. Finally, the corresponding numerical simulation and comparative analysis are performed on two test systems to verify the validity of the proposed model and method.

A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observer (P&O) under Partial and Complex Partial Shading Conditions

Photovoltaic (PV) systems have been used extensively worldwide over the past few years due to the mitigation of fossils fuels; it is the best source because of its eco-friendly nature. In PV systems, the main research area concerns its performance under partial shading (PS) and complex partial shading (CPS) conditions. PV sources perform perfectly under ideal conditions, but under practical conditions, their performance depends upon many factors, including shading conditions, temperature, irradiance, and the angle of inclination, which can bring a photovoltaic or solar system into a PS or CPS condition. In these conditions, many power peaks appear, and it is hard to find the global peak among many local peaks. The ability to track the maximum power peak and maintain it to avoid fluctuations depends on the maximum power point tracking (MPPT) technique used in a photovoltaic system. This article is based on the implementation of a hybrid algorithm, combining Harris hawk’s optimization (HHO), a new technique which is based on natural inspiration, and a conventional perturb and observe (P&O) technique. The hybrid technique was tested under different weather conditions in MATLAB Simulink and showed less computational time, a fast convergence speed, and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the hybrid technique was made with bio-inspired particle swarm optimization (PSO), adaptive cuckoo search optimization (ACS), the dragonfly algorithm (DFO), and the water cycle algorithm (WCA). The hybrid technique achieves 99.8% efficiency on average and performs very well among the rest of the competing techniques.

Multi-strategy adaptive cuckoo search algorithm for numerical optimization

Multi-strategy adaptive cuckoo search algorithm for numerical optimization