Standard Cuckoo Search Algorithm Research Articles

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

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.

A novel method for identifying recursive systematic convolutional encoders based on the cuckoo search algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively. With the increase of the codeword length and constraint length, the search space expands exponentially, and thus it limits the application of these methods in practice. To overcome the limitation, a novel identification method, which gets rid of exhaustive test, is proposed based on the cuckoo search algorithm by using soft-decision data. Firstly, by using soft-decision data, the probability that a parity check equation holds is derived. Thus, solving the parity check equations is converted to maximize the joint probability that parity check equations hold. Secondly, based on the standard cuckoo search algorithm, the established cost function is optimized. According to the final solution of the optimization problem, the generator matrix of recursive systematic convolutional code is estimated. Compared with the existing methods, our proposed method does not need to search for the generator matrix exhaustively and has high robustness. Additionally, it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

Multi-objective water resources optimum allocation scheme based on an improved standard cuckoo search algorithm (ISCSA)

Abstract The standard cuckoo search algorithm (SCSA) is an intelligent population optimization algorithm, which is also a heuristic search algorithm. The advantages of the SCSA (such as its convenient operation, heuristic searching, etc.) make it easy to find an optimal solution and maintain a wide searching range. However, the SCSA also has some drawbacks, such as long searching time, and the ease of falling on a local optimum. In order to solve the problems existing with SCSA, in this paper, an improved standard cuckoo search algorithm (ISCSA) was studied, which includes chaotic initialization and a Gaussian disturbance algorithm. As a case study, taking economic, social and ecological benefits as the objective function, multi-objective water resources optimal allocation models were constructed in Xianxiang Region, China. The ISCSA was applied to solve the water allocation models and a multi-objective optimal water supply scheme for Xinxiang region was obtained. Water resources optimal allocation schemes for the planning level year (2025) for 12 water supply sub-regions were predicted. A desirable eco-environment and other benefits were achieved using the studied methods. The results show that the ISCSA has obvious advantages in the solution of water resources optimal allocation and planning.

Optimal reservoir operation using parametric elitist cuckoo search algorithm

Optimal reservoir operation has become a challenging problem due to streamflow uncertainties. Frameworks for optimal reservoir operation play a vital role in the management of water resources. Effective and judicious utilisation of water from reservoirs helps in the management of periods of water deficit. The main objective in reservoir optimisation is to design operating rules that can be used to derive real-time decisions on reservoir release. In this study, a metaheuristic optimisation algorithm – the parametric elitist cuckoo search algorithm (Pecsa), based on the basic cuckoo search algorithm (CSA) – was applied to develop optimal operation decisions for the RSSR in the MRP, India. To evaluate the performance of Pecsa, the results obtained were compared with another heuristic method (the time-variant elitist mutation particle swarm optimisation algorithm) and with the standard CSA and particle swarm optimisation methods. Pecsa was found to improve system operation and outperformed the other methods in terms of achieving a faster convergence rate. Pecsa also showed only minor variations in the optimal pattern of release policies compared with those obtained using the standard CSA. The implications of the results and suggestions for further research are discussed.

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.

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.

A Cuckoo Search Algorithm Using Improved Beta Distributing and Its Application in the Process of EDM

Lévy flights random walk is one of key parts in the cuckoo search (CS) algorithm to update individuals. The standard CS algorithm adopts the constant scale factor for this random walk. This paper proposed an improved beta distribution cuckoo search (IBCS) for this factor in the CS algorithm. In terms of local characteristics, the proposed algorithm makes the scale factor of the step size in Lévy flights showing beta distribution in the evolutionary process. In terms of the overall situation, the scale factor shows the exponential decay trend in the process. The proposed algorithm makes full use of the advantages of the two improvement strategies. The test results show that the proposed strategy is better than the standard CS algorithm or others improved by a single improvement strategy, such as improved CS (ICS) and beta distribution CS (BCS). For the six benchmark test functions of 30 dimensions, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 3.67, 2.67, 1.5, and 1.17, respectively. For the six benchmark test functions of 50 dimensions, moreover, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 2.83, 2.5, 1.67, and 1.0, respectively. Confirmed by our case study, the performance of the ABCS algorithm was better than that of standard CS, ICS or BCS algorithms in the process of EDM. For example, under the single-objective optimization convergence of MRR, the iteration number (13 iterations) of the CS algorithm for the input process parameters, such as discharge current, pulse-on time, pulse-off time, and servo voltage, was twice that (6 iterations) of the IBCS algorithm. Similar, the iteration number (17 iterations) of BCS algorithm for these parameters was twice that (8 iterations) of the IBCS algorithm under the single-objective optimization convergence of Ra. Therefore, it strengthens the CS algorithm’s accuracy and convergence speed.

Improved discrete cuckoo‐search algorithm for mixed no‐idle permutation flow shop scheduling with consideration of energy consumption

The increasing number and types of industrial tasks require factories to be more flexible in production. An improved discrete cuckoo search algorithm (CSA) is proposed and used to optimise the mixed no-idle permutation flow shop scheduling problem (MNPFSP). This problem considers MNPFSP energy consumption (MNPFSP_EC) an optimisation objective. Firstly, according to the characteristics of the individual update formula in the two stages of the standard CSA, the paper replaces the real number calculation or vector calculation in the original update formula with a discrete operation to keep the update mechanism of each stage unchanged. The change allows the algorithm to directly find a feasible solution in the discrete solution space that significantly improves the global search capability of cuckoo search. Secondly, an adaptive-starting local search based on quasi-entropy (QE) is constructed using swap, insert and 2-OPT operations with an exploitation that is adaptively executed based on QE, and QE is used to represent the diversity of population and control individuals in deciding whether to execute local search, thereby reducing computational complexity. Simulation experiments and comparisons of different instances demonstrate that the proposed algorithm can effectively solve MNPFSP_EC.

An improved cuckoo search algorithm for multilevel color image thresholding based on modified fuzzy entropy

Multilevel thresholding is a significant technology for image segmentation. Traditional exhaustive search method to solve the optimal multilevel thresholds is time consuming, color images contain more information are even worse. To address this issue, an improved cuckoo search algorithm (ICS) is proposed for color image segmentation in this paper, and a modified fuzzy entropy is used as its objective function. In ICS, two modifications are adopted to improve the standard cuckoo search algorithm. First, an adaptive control parameter mechanism is utilized to enhance the performance of exploration. Second, a hybrid search strategy is used to boost the local search efficiency. To fully demonstrate the superior performance of the ICS, the experiments are conducted on a series of color benchmark test images, and a total of six optimization algorithms are compared with the proposed algorithm. The experimental results show that the presented ICS algorithm outperforms all the other algorithms in term of objective function value, PSNR, FSIM, convergence speed and parametric statistical test. Compared to other algorithms, the ICS algorithm is an effective method for multilevel color image thresholding segmentation.

An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions

The problem of partial shading has serious effects on the performance of photovoltaic (PV) systems. Adding a bypass diode in shunt to each PV module avoids hot-spot phenomena, but causes multi-peaks in the power–voltage (P–V) characteristics of the PV array, which cause traditional maximum power point tracking (MPPT) techniques to become trapped in local peaks. This problem has forced researchers to search for smart techniques to track global peaks and prevent the possibility of convergence at local peaks. Swarm optimization techniques have been used to fill this shortcoming; unfortunately, however, these techniques suffer from unacceptably long convergence time. Cuckoo search (CS) is one of the fastest and most reliable optimization techniques, making it an ideal option to be used as an MPPT of PV systems under dynamic partial shading conditions. The standard CS algorithm has a long conversion time, high failure rate, and high oscillations at steady state; this paper aims to overcome these problems and to fill this research gap by improving the performance of the CS. The results obtained from this technique are compared to five swarm optimization techniques. The comparison study shows the superiority of the improved CS strategy introduced in this paper over the other swarm optimization techniques.

Multilevel thresholding using an improved cuckoo search algorithm for image segmentation

Multilevel thresholding image segmentation is an important technique, which has attracted much attention in recent years. The conventional exhaustive search method for image segmentation is efficient for bilevel thresholding. However, they are time expensive when dealing with multilevel thresholding image segmentation. To better tackle this problem, an improved cuckoo search algorithm (ICS) is proposed to search for the optimal multilevel thresholding in this paper, and Otsu is considered as its objective function. In the ICS, two modifications are used to improve the standard cuckoo search algorithm. First, a parameter adaptation strategy is utilized to improve exploration performance. Second, a dynamic weighted random-walk method is adopted to enhance the local search efficiency. A total of six benchmark test images are used to perform the experiments, and seven state-of-the-art metaheuristic algorithms are introduced to compare with the ICS. A series of measure indexes such as objective function value and standard deviation, PSNR, FSIM, and SSIM as well as the Wilcoxon rank sum and convergence performance are performed in the experiments; the experimental results show that the proposed algorithm is superior to other seven well-known heuristic algorithms.

Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization

Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).

Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.

Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

The cuckoo search algorithm (CSA) is a promising metaheuristic algorithm for solving numerous problems in different fields. It adopts the Levy flight to guide the search process. Nonetheless, CSA has drawbacks, such as the utilization of global search; in certain cases, this technique may surround local optima. Moreover, the results cannot be guaranteed if the step size is considerably large, thereby leading to a slow convergence rate. In this study, we introduce a new method for improving the search capability of CSA by combining it with the bat algorithm (BA) to solve numerical optimization problems. The proposed algorithm, called CSBA, begins by establishing the population of host nests in standard CSA and then obtains a solution through particular part to identify a new solution in BA (i.e., further exploitation). Therefore, CSBA overcomes the slow convergence of the standard CSA and avoids being trapped in local optima. The performance of CSBA is validated by applying it on a set of benchmark functions that are divided into unimodal and multimodal functions. Results indicate that CSBA performs better than the standard CSA and existing methods in the literature, particularly in terms of local search functions.

Personal Best Cuckoo Search Algorithm for Global Optimization

Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced explorative and exploitative capabilities. Cuckoo search (CS) algorithm is also one of such optimization algorithms, which is inspired from nature. Despite effective search strategies using Levy flights and solution switching approach, CS suffers from lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in CS algorithm. The proposed variant employs personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter as the iterations proceed. The prior approach enhances local search ability, whereas the later modification enforces effective global search ability in the algorithm. The experimental results on both unimodal and multimodal test functions with different dimensionalities validated the efficiency of the proposed modification. Based on comprehensive statistical analysis and comparisons, pBestCS outperformed the standard CS algorithm, as well as, other popular swarm-based metaheuristic algorithms particle swarm optimization (PSO) and artificial bee colony (ABC).

A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise

In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA.

Applying modified cuckoo search algorithm for solving systems of nonlinear equations

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 fitness-sharing principle. Niche strategy is introduced to enhance the ability of the cuckoo search algorithm to solve nonlinear equations. So as to evaluate the efficiency of NCSA, NCSA has been first benchmarked by twenty standard test functions by comparing with standard genetic algorithm, chaos gray-coded genetic algorithm and standard cuckoo search algorithm. Then, solutions for several examples of nonlinear systems are presented and compared with results obtained by other approaches. Moreover, the sensitivity analysis of the method to initial interval, nests number, probability and niche number has been studied. Comparison results reveal that the proposed algorithm can cope with the highly nonlinear problems and outperforms many algorithms which exist in the literature.

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.