Adaptive Cuckoo Search Algorithm Research Articles

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.

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.

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.

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.

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.

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.

Multi-strategy adaptive cuckoo search algorithm for numerical optimization

Multi-strategy adaptive cuckoo search algorithm for numerical optimization

Time-Jerk optimal Trajectory Planning of Industrial Robots Based on a Hybrid WOA-GA Algorithm

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and achieved the time-jerk optimal trajectory better under kinematic constraints compared to the genetic algorithm, whale optimization algorithm, improved whale optimization algorithm with particle swarm optimization and adaptive cuckoo search algorithm. The numerical results show the competent performances of the proposed methodology to generate trajectories with high smooth curves and short total execution time.

A ranking-based adaptive cuckoo search algorithm for unconstrained optimization

Cuckoo search (CS) has been proven to be one of the most efficient metaheuristic algorithms in solving global optimization problems. However, it suffers from a slow convergence speed and premature convergence, especially when the complexity of the problem increases. To address these shortcomings, a ranking-based adaptive cuckoo search algorithm, called RACS, is proposed in this paper. Specifically, a novel ranking-based mutation strategy is designed at first, which is inspired by the natural phenomenon that good species or individuals always contain good information and thus have better odds of guiding others. In the proposed ranking-based mutation strategy, the global search equation is modified in combination with a ranking-based vector selection method, where some of the parent vectors are proportionally selected according to their rankings. The higher ranking a parent vector obtains, the more opportunity it will be chosen. Secondly, a crossover operation with parameter adaptation is employed after the Lévy flights random walk to preserve some good elements of the current solutions from being changed. Furthermore, a replacement strategy is designed to update the solutions not improved through pre-determined cycles by exploiting the beneficial information from the discarded solutions saved in the external archive. To evaluate the comprehensive performance of RACS, extensive experiments are conducted on three well-known test suites and an application problem of identifying unknown parameters of fractional-order nonlinear systems. Simulation results demonstrate that the presented strategies bring a significant improvement in effectiveness and efficiency on CS. Besides, RACS is verified to be superior or at least comparable to other CS variants and state-of-the-art algorithms on most of the benchmark problems, and thus, can be regarded as a useful and promising technique for solving real-world complex optimization problems.

Sensitive Vector Search for Logic Circuit Failure Probability based on Improved Adaptive Cuckoo Algorithm

With the development of microelectronics technology, the feature size of integrated circuit continues to shrink, and circuit performance has been improved. At the same time, however, factors such as process disturbance, power noise, and particle radiation are having an increasingly serious influence on the Failure Probability of Circuits (FPC). Searching the input vectors that are sensitive to FPC can assist circuit designers in selectively reinforcing the circuit to reduce the fault-tolerant overhead and improve the fault-tolerant efficiency. In this paper, an Improved Adaptive Cuckoo Search (IACS) algorithm is proposed to search sensitive circuit vectors. The vector segmentation strategy is used to change the dimension of the input vector, the hill climbing algorithm is used to improve the quality of the initial population, and the adaptive strategy is used to control parameters such as power-law index, discovery probability and scaling factor. At the same time, a Correlation Separation Approach (COSEA) is proposed to calculate the FPC under specific vector excitation. Experimental results show that the proposed algorithm has higher accuracy and better efficiency compared with existing algorithms.

WSN Node Based on Adaptive Cuckoo Search Algorithm for Agricultural Broadcast Positioning

The node location of wireless sensor network (WSN) is actually a multi-dimensional constraint optimization problem for measuring distance and range error. A new adaptive cuckoo search algorithm is proposed to solve the problems of the standard cuckoo search algorithm, such as slow convergence rate and easy to get into local optimum. Firstly, the algorithm has a large searching space in the early stage and improves the global searching ability by adjusting the flight step length of Levy. Secondly, dynamic inertial weight and memory strategy are introduced for random swimming; therefore the algorithm can make full use of historical experience and improve the stability. Finally, simulation results show that the proposed algorithm can effectively improve the positioning accuracy without increasing the hardware cost.

High-Accuracy 3D Indoor Visible Light Positioning Method Based on the Improved Adaptive Cuckoo Search Algorithm

High-Accuracy 3D Indoor Visible Light Positioning Method Based on the Improved Adaptive Cuckoo Search Algorithm

Adaptive Cuckoo Search Algorithm Based on Dynamic Adjustment Mechanism

Cuckoo Search (CS) algorithm, a simple and effective global optimization algorithm, has been widely used to deal with practical optimization problems. So as to improvethe standard cuckoo search algorithm, such as slow convergence and easy convergence to local optimal value, an Adaptive Cuckoo Search algorithm on the basis of Dynamic Adjustment Mechanism (ACSDAM) has been proposed. Based on exponential function and logarithmic function, the dynamic adjustment is made for updating step size and discovering probability. During the optimization process, updating step size and discovering probability of each nest are adjusted according to the number of iterations of each nest, so as to equilibrate the global detection and local capacity of the algorithm. Then 23 standard test functions will be selected for a simulation experiment, and compared with other CS variant algorithms, ACS-DAM effectively improved the rate of convergence and the algorithmic precision. ACS-DAM algorithm was employed to optimize the Support Vector Machine (SVM). The experiment proves that the convergence rate with ACSDAM is better than that with CS obviously and ACS-DAM has stronger optimization ability and higher efficiency than CS.

Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm

Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm

Optimal operation of wind-hydrothermal systems considering certainty and uncertainty of wind

This paper proposes a High Performance Cuckoo Search Algorithm (HPCSA) for determining suitable operation parameters of the optimal wind-hydro-thermal system scheduling (OWHTSS) problem. The objective of the problem is to reach the lowest electricity generation cost of thermal power plants (TPPs) and wind power plants (WPPs) while exactly meeting all constraints of TPPs, WPPs and hydroelectric plants (HEPs). HPCSA is formed by applying improvements on the two main techniques of original Cuckoo Search Algorithm (CSA) to cover CSA’ drawbacks such as searching random solution spaces, always using two random solutions for getting a jumping step and suffering from slow convergence. HPCSA accompany with CSA, Adaptive CSA (ACSA), Snap-Drift CSA (SDCSA) and Water Cycle Algorithm (WCA) are run for solving four test systems in which the largest and complicated system is comprised of four TPPs, four HEPs and two WPPs with the uncertain wind feature. The result comparisons indicate that HPCSA is superior to applied and previous methods, and other modified versions of CSA in the literature in terms of better cost, higher stability, faster search ability and higher success rate. As a result, it leads to a conclusion that HPCSA is a strong metaheuristic algorithm for solving OWHTSS problem.

Model driven test case generation and optimization using adaptive cuckoo search algorithm

Software testing is leading toward automation that reduces the effort to find errors or bugs. The identification of test cases and its critical domain requirements is done with generation of test cases. The brooding characteristic of the cuckoo bird is explained through the adaptive cuckoo search meta-heuristic algorithm (ACSA) that further narrates that host nest is used by the cuckoo bird for laying their eggs and the next generation also sees the best quality eggs from the host bird’s nest. This paper focuses on the adoption of ACSA for analysis, generation, and optimization of random test cases. In addition to that, the present work also explains the model driven approach to automatically generate and optimize the test cases with the help of unified modeling language diagram like sequence diagram. Then, the respective sequence diagram is converted into a sequence diagram graph that shows the flow of sequences being produced. Thereafter, it is optimized using ACSA by taking a case study of withdrawal operation of ATM transaction. The said approach is also evaluated in terms of efficiency and usefulness for generating the test cases through simulated experiments. In addition to that, the projected approach also identifies the operational faults as well as message faults.

An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm

It is the most challenging and difficult task to segment a satellite image because of its complete randomness, multiple regions of interest, weak correlation with pixels, and regions of ambiguity. There are several Nature-inspired algorithms available, which are used to overcome these difficulties and those are more efficient to generate the best threshold value for the segmentation of satellite images. Though various modern methodologies opt for better results but methods have some drawbacks too like techniques are computationally expensive and time-consuming. In this paper, we have proposed a more effective satellite image segmentation approach using a new adaptive cuckoo search (ACS) algorithm. The result obtained from the projected technique is compared with CSMcCulloch incorporating McCulloch’s method for levy flight generation in Cuckoo Search (CS) algorithm by using two different objective functions namely Otsu’s method and Tsallis entropy function. The measurement techniques such as PSNR, MSE, FSIM, SSIM, UIQI, and computational time in term of CPU running time have been considered for validating and evaluating the proposed method. This proposed algorithm technique has resulted in improve segmentation quality of satellite images and reduced computational time. The analysis of the convergence rate proves that ACS is superior to the CSMcCulloch algorithm for reaching the global convergence rate. These experimental outcomes help to encourage researchers in different domains such as computer vision, application of medical image analysis, machine learning as well as deep learning.

Quality Prediction of Fused Deposition Molding Parts Based on Improved Deep Belief Network.

Tensile strength, warping degree, and surface roughness are important indicators to evaluate the quality of fused deposition modeling (FDM) parts, and their accurate and stable prediction is helpful to the development of FDM technology. Thus, a quality prediction method of FDM parts based on an optimized deep belief network was proposed. To determine the combination of process parameters that have the greatest influence on the quality of FDM parts, the correlation analysis method was used to screen the key quality factors that affect the quality of FDM parts. Then, we use 10-fold cross-validation and grid search (GS) to determine the optimal hyperparameter combination of the sparse constrained deep belief network (SDBN), propose an adaptive cuckoo search (ACS) algorithm to optimize the weights and biases of the SDBN, and complete the construction of prediction model based on the above work. The results show that compared with DBN, LSTM, RBFNN, and BPNN, the ACS-SDBN model designed in this article can map the complex nonlinear relationship between FDM part quality characteristics and process parameters more effectively, and the CV verification accuracy of the model can reach more than 95.92%. The prediction accuracy can reach more than 96.67%, and the model has higher accuracy and stability.

Shape optimization of generalized developable H-Bézier surfaces using adaptive cuckoo search algorithm

The shape optimization design of developable surface is a critical and intractable technique in CAD/CAM and used in many manufacturing planning operations. In this paper, we study shape optimization by using a new cuckoo search (CS) algorithm to promote the development of generalized developable H-Bézier surfaces (or GDHBS, for short). According to the duality between points and planes in 3D projective space, the GDHBS shape optimization problem is formulated as an arc length, energy and curvature variation minimization problem of dual curve, respectively. Then, an adaptive CS optimization algorithm is applied to the solutions of those GDHBS shape optimization models. Finally, we illustrate the effectiveness and performance of the proposed methods by some representative and convictive examples.