Improved Cuckoo Algorithm Research Articles

Collaborative Optimization of Vessel Stowage Planning and Yard Pickup in Automated Container Terminals

The aim of this research is to minimize the number of container-rehandling operations in both the yard and on the ship in order to solve the problem of coordinated optimization of ship loading and yard container retrieval and enhance the loading efficiency of automated container terminals,. An optimization model that integrates the optimized decision-making of both the container retrieval order from the yard and the ship’s space allocation is developed, and an improved cuckoo algorithm is employed to solve the optimization problem. This paper examines the influence of container retrieval order from the yard on rehandling within the yard, as well as the effect of the ship’s space allocation on subsequent rehandling operations at the unloading port. Experimental results demonstrate that the solution derived from the comprehensive optimization model based on the improved cuckoo algorithm considerably reduces the overall number of overturned containers. This confirms that the proposed optimization model effectively enhances loading efficiency and reduces container rehandling both in the yard and on the ship. The analysis of experimental results indicates that the model and algorithm proposed in this paper have certain applicability.

Photovoltaic MPPT based on improved Cuckoo algorithm

Abstract Photovoltaic power generation is susceptible to external factors such as light, resulting in a reduction in the actual efficiency of photovoltaic power generation. However, the traditional maximum power point tracking technology has the characteristics of slow convergence speed and poor accuracy. In order to improve the above problems, a new photovoltaic maximum power control algorithm with an improved cuckoo algorithm is proposed. By adjusting the iterative step size in the cuckoo algorithm and changing the probability of finding the bird nest, the iterative convergence speed is accelerated, and the global optimization ability is increased. Finally, the algorithm is applied to the maximum power tracking, and the simulation model is built on the Matlab/Simulink platform. After comparing the standard Cuckoo algorithm with the improved Cuckoo algorithm under static and dynamic conditions, the simulation results show that the improved Cuckoo algorithm has more advantages in maximum power tracking speed and accuracy.

Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved Cuckoo Algorithm

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensitive to load changes. The solution iteration of the cuckoo algorithm is 18 times. Meanwhile, the system requires four updates of capacity allocation results for 20 years of operation. This result validates the effectiveness of the proposed model and methodology. It also provides a reference for the research and construction of capacity allocation of microgrid systems at the park level.

Elastic Tracking Operation Method for High-Speed Railway Using Deep Reinforcement Learning

Transportation-related consumer electronics technology has advanced rapidly, particularly for automated train operation on high-speed railways. To maximize transport capacity and meet growing demands, this manuscript proposes a new elastic tracking operation control method, that compresses the tracking interval while maintaining safety. The train operation process is formulated as a Monte Carlo process and the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is used to generate the basic operation strategy. A three-stage control principle and train tracking operation requirements are taken into account, and an elastic parameter-based train state transition rule is proposed. An improved cuckoo algorithm is then used to determine the elastic parameters for faster and more accurate solution convergence. Our results demonstrate that TD3-TOC is effective in i) improving the stability of the train operation process, ii) reducing the tracking interval, and iii) reducing delay in the case of emergency. In addition, the effectiveness of the elastic interval is demonstrated in experiments.

Application of the Improved Cuckoo Algorithm in Differential Equations

To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals through the utilization of a sharing mechanism. Additionally, new search strategies are introduced in both the global and local searches of the CS. The results from numerical experiments on four standard test functions indicate that the improved algorithm outperforms the original CS in terms of search capability and performance. Building upon the improved algorithm, this paper introduces a numerical solution approach for differential equations involving the coupling of function approximation and intelligent algorithms. By constructing an approximate function using Fourier series to satisfy the conditions of the given differential equation and boundary conditions with minimal error, the proposed method minimizes errors while satisfying the differential equation and boundary conditions. The problem of solving the differential equation is then transformed into an optimization problem with the coefficients of the approximate function as variables. Furthermore, the improved cuckoo search algorithm is employed to solve this optimization problem. The specific steps of applying the improved algorithm to solve differential equations are illustrated through examples. The research outcomes broaden the application scope of the cuckoo optimization algorithm and provide a new perspective for solving differential equations.

Coverage and Routing Optimization of Wireless Sensor Networks Using Improved Cuckoo Algorithm

Coverage and Routing Optimization of Wireless Sensor Networks Using Improved Cuckoo Algorithm

Heterogeneous Traffic Flow Signal Control and CAV Trajectory Optimization Based on Pre-Signal Lights and Dedicated CAV Lanes

This paper proposes a control system to address the efficiency and pollutant emissions of heterogeneous traffic flow composed of human-operated vehicles (HVs) and connected and automated vehicles (CAVs). Based on the comprehensive collection of information on the flow of heterogeneous traffic, the control system uses a two-layer optimization model for signal duration calculation and CAV trajectory planning. The upper model optimizes the phase duration in real time based on the actual total number and type of vehicles entering the control adjustment zone, while the lower model optimizes CAV lane-changing strategies and vehicle acceleration optimization curves based on the phase duration optimized by the upper model. The target function accounts for reducing fuel usage, carbon emission lane-changing costs, and vehicle travel delays. Based on the Webster optimal cycle formula, an improved cuckoo algorithm with strong search performance is created to solve the model. The numerical data confirmed the benefits of the suggested signal control and CAV trajectory optimization method based on pre-signal lights and dedicated CAV lanes for heterogeneous traffic flow. Intersection capacity was significantly enhanced, CAV average fuel consumption, carbon emission and lane-changing frequency were significantly reduced, and traffic flow speed and delay were significantly improved.

Rate-Dependent Hysteresis Model of a Giant Magnetostrictive Actuator Based on an Improved Cuckoo Algorithm

A rate-dependent asymmetric Prandtl–Ishilinskii (RAPI) model was proposed to tackle the serious rate-dependent hysteresis nonlinearity of the giant magnetostrictive actuator(GMA) output. First, a polynomial function was introduced based on the PI model, and hysteresis factors were introduced to the Play operator, which accurately described the asymmetrical characteristic of the actuator output. On this basis, rate-dependent parameters were added to establish a rate-dependent RAPI model. Second, an improved cuckoo search (ICS) algorithm was proposed to solve the difficulty in the parameter identification of the RAPI model. For the ICS algorithm, the algorithm stability and optimization accuracy were improved using the adaptive step (AS) strategy and bird’s nest disturbance strategy. Then, the effectiveness of the ICS algorithm was tested by comparing it with other parameter identification algorithms. Finally, the rate-dependent RAPI model was verified by combining the output data of the giant magnetostrictive actuator under different frequencies. The results show that the rate-dependent RAPI model exhibits a higher accuracy than the PI model, thus verifying the effectiveness of the rate-dependent RAPI model.

Research on the extraction method of traditional elements in environmental art space design based on intelligent algorithm

Abstract This paper builds a spatial design model based on the reconstruction of 3D visual spatial points in an indoor environment and combines the minimum Euclidean distance to calculate the vector relationship between spatial points and realize the corresponding spatial point matching. For the exploration of traditional elements in spatial design, an extraction method based on the improved cuckoo algorithm is proposed. By adding a dynamic inertia weight adjustment method, one inertia weight value is randomly selected from the Gaussian distribution each time to balance the algorithm’s local and global search. By extracting traditional elements, calligraphy is the most frequent in the home space, with 79.4% integration. The tea set is the most frequent traditional element in the dining space, with 84.53% integration. A couplet is the most frequent traditional element in the public open space, with 71.98% integration. The extraction based on intelligent algorithms can help designers better integrate traditional elements in the design of environmental spaces.

Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network

The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.

Optimization of operation and maintenance path of offshore wind turbine based on ICS-BP

An ICS-BP model for the operation and maintenance of the path optimization method is proposed to solve the path optimization problem based on the actual operation and maintenance of offshore wind turbines. The model comprehensively considers the impact of offshore wind speed and waves on the operation and maintenance ships, obtains the equivalent path matrix through the BP neural network model, and optimizes the path through the improved Cuckoo algorithm search (ICS). To verify the model’s effectiveness, the data of an offshore wind field is used, and the optimized path is greatly improved compared with other methods.

Retracted: Power System Reactive Power Optimization Sensor Dispatching Digital Grid Based on Improved Cuckoo Algorithm

Retracted: Power System Reactive Power Optimization Sensor Dispatching Digital Grid Based on Improved Cuckoo Algorithm

An improved cuckoo search algorithm for the hybrid flow-shop scheduling problem in sand casting enterprises considering batch processing

To solve the hybrid flow-shop scheduling problem in sand casting enterprises with multivariate and multistage production characteristics, an improved cuckoo algorithm was developed. First, considering the “single-batch” coupling relationship in the production process, a single-layer coding rule based on the sorting of casting category numbers was designed, and the corresponding decoding mechanism was designed in the batch stage and single-piece processing stage. Second, crossover and mutation operations were introduced to replace the long and short flight strategies, which can improve the search ability of the cuckoo algorithm. In addition, the strategy of separating superior and inferior populations was applied to accelerate the evolution of the population. Finally, the values of the algorithm parameters were optimized by conducting orthogonal experiments, and the rationality of the scheduling model and the superiority of the improved algorithm were verified through simulations.

An Improved Cuckoo Search Algorithm Based on Inertial Weight and Scaling Factor

<p>This paper presents a novel and an improved cuckoo search algorithm (ISCS), different from other techniques, which sets nonlinear decreasing inertia weight and adaptive scaling factor. As the iteration goes on, these two parameters are controlled by two functions to change the iterations dynamically. At the beginning of the iteration, the values of the two parameters are favorable for global search, and at the end of the iteration, their values are more favorable for local optimization. In this work, 23 classical benchmark functions are selected to improve the accuracy and convergence speed by conducting the simulation experiments on CS, ISCS and other three algorithms. The results show that the improved cuckoo algorithm enhances the accuracy of understanding and speeds up the convergence of the curves. Finally, ISCS is applied to probabilistic neural network (PPN) neural network classification and recognition technology, and the results show that the optimization of ISCS can effectively improve the classification accuracy of the test sets, and has diverse applications.</p> <p> </p>

Research on Hyper-Parameter Optimization of Activity Recognition Algorithm Based on Improved Cuckoo Search

Activity recognition methods often include some hyper-parameters based on experience, which greatly affects their effectiveness in activity recognition. However, the existing hyper-parameter optimization algorithms are mostly for continuous hyper-parameters, and rarely for the optimization of integer hyper-parameters and mixed hyper-parameters. To solve the problem, this paper improved the traditional cuckoo algorithm. The improved algorithm can optimize not only continuous hyper-parameters, but also integer hyper-parameters and mixed hyper-parameters. This paper validated the proposed method with the hyper-parameters in Least Squares Support Vector Machine (LS-SVM) and Long-Short-Term Memory (LSTM), and compared the activity recognition effects before and after optimization on the smart home activity recognition data set. The results show that the improved cuckoo algorithm can effectively improve the performance of the model in activity recognition.

Optimized Rod Size Design of Denim Fabric Grinding Robot Based on Improved Cuckoo Search Algorithm

Background: Dexterity is an important index for evaluating the motion performance of a robot. The size of the robot connecting rods directly affects the performance of flexibility. Objective: The purpose of this study is to provide an overview of optimal design methods from many pieces of literature and patents, and propose a new optimal design method for ensuring the robot completes its tasks flexibly and efficiently under workspace constraints. Methods: The kinematics and working space of the robot are analyzed to determine the range of motion of each joint. Then, a dexterity index is established based on the mean value of the global spatial condition number. Finally, an improved cuckoo algorithm is proposed, which changes the step size control factor with the number of iterations. Taking the dexterity index as the objective optimization function and the working radius as the constraint condition, the improved cuckoo search algorithm is used to optimize the size of the robot rod. Results: The improved cuckoo algorithm and proposed rod size optimized method are fully evaluated by experiments and comparative studies. The optimization design process shows that the proposed method has better solution accuracy and faster convergence speed. The optimized design results show that the robot's dexterity index has increased by 26.1%. Conclusion: The proposed method has better solution accuracy and faster convergence speed. The method was suitable for optimizing the rod parameters of the robot, and it was very meaningful to improve the motion performance of the robot.

An RNN-MCDA method to support efficient and stable matching under uncertain preferences in large-scale sharing platforms

The sharing economy has promoted the rapid development of the global economy, the stable matching plays a vital role in resource sharing. However, the large-scale platform increases the difficulties of stable matching, like low efficiency and weak matching. To realize stable and efficient matching in a large-scale platform, our study designs a recurrent neural network-multiple criteria decision aiding (RNN–MCDA) method. Firstly, to avoid time-consuming mutual assessment in large-scale matching, the RNN–MCDA method is proposed to learn and predict the demanders’ and suppliers’ (DAS) uncertain preferences, like linguistic preferences, which are frequently used in sharing platforms. Considering the disadvantages of low accuracy and interpretability in predicting linguistic preferences in previous studies, we combine the RNN and MCDA to learn and predict the linguistic preferences of DAS. Then, for the matching mechanism, to realize the stable matching, we design a two-stage matching mechanism based on RNN–MCDA. At the first stage, we aim at maximizing total satisfaction based on RNN–MCDA instead of inefficient mutual assessment. For the second stage, we focus on the individuals who do not meet the stable constraint in the first stage and use the platforms’ strategies to adjust the satisfaction of DAS and realize stable matching. Finally, an improved cuckoo algorithm is designed to solve the bi-level programming. An example in the manufacturing capacity sharing platform is used to verify our study.

Power System Reactive Power Optimization Sensor Dispatching Digital Grid Based on Improved Cuckoo Algorithm

In order to ensure the economic, safe, and stable operation of the power grid in the power system, the author proposes a power system reactive power optimization sensor dispatching digital grid based on an improved cuckoo algorithm. Use this improved cuckoo algorithm to optimize the solution, optimal dispatch model of power system considering economy and environmental protection. The main improvement strategies are as follows: on the basis of the original algorithm, the exchange operator in the particle swarm algorithm is introduced, promoting communication between bird’s nests; at the same time, two fixed parameters of probability and step size will be found and set to dynamic quantity to improve the convergence speed of the algorithm; in the process of updating the location of the bird’s nest, use nondominant sorting and calculation of crowding distance to maintain the archives; after generating the optimal solution, use the satisfaction evaluation theory of fuzzy mathematics to obtain the optimal solution. The result said that the cost of power generation and pollutant emissions is USD 125.1 million/h and 0.3361 t/h, respectively. Compared with the optimization results of the other three traditional algorithms, power generation costs and pollutant emissions reduced. The algorithm shows that the improved algorithm has better optimization speed and accuracy, and at the same time, it can effectively improve the global convergence, to ensure the diversity of Pareto’s noninferior solutions.

Improved Cuckoo Algorithm for the Hybrid Flow-Shop Scheduling Problem in Sand Casting Enterprises Considering Batch Processing

Improved Cuckoo Algorithm for the Hybrid Flow-Shop Scheduling Problem in Sand Casting Enterprises Considering Batch Processing

Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

With the extensive application of unmanned aerial vehicles (UAVs), there is an increasing demand for fast processing of coloured UAV images. The coloured UAV image pixels are usually represented by quaternion vectors with three bands of visible light corresponding to the three imaginary parts of the pure imaginary quaternion. Accordingly, the colour image edge points can be determined based on the quaternion polar coordinating the rotation principle. Here, a quaternion-based improved cuckoo algorithm is proposed to perform fast processing for UAVs images. In particular, a novel guiding equation is used to optimize the positions of the improved cuckoo algorithm before the Levi flight. Furthermore, a novel disturbance equation is used to obtain a varied location for the next location after the Levi flight. Comprehensive experiments are conducted to evaluate the performance of the proposed solution. The experimental results showed that the proposed method significantly reduces the image processing time and remarkably improves the quality.