Adaptive Cuckoo Search Algorithm Research Articles

Multi-objective economic operation of modern power system considering weather variability using adaptive cuckoo search algorithm

Currently, most of the power systems are being integrated with flexible AC transmission system devices and renewable energy sources for operating with enhanced security margins and balancing the increasing demand cost-effectively. On the other side, the trend of increasing global warming and extremely changing weather conditions is continuing across the world. Under this scenario, it is essential to realize their effect on various power system components and its economic operation. In this paper, the parameters namely resistance of the transmission line/transformer, load and solar photovoltaic generation are modeled considering ambient temperature effect. Later, economic schedule under changing weather conditions is proposed for attaining multi-objectives simultaneously like total operating cost of conventional energy, real power loss, average voltage collapse point indicator index and average voltage deviation index. Also, the dispatchable problems in the transmission system and various practical operating constraints are handled via optimally setting the parameters of optimal unified power flow controller. The optimization problem is solved using adaptive cuckoo search algorithm (ACSA), in which a dynamically increasing switching parameter in a power of three is adopted for adjusting the random walk between local optima and global optima. The superiority of the proposed ACSA in solving the multiobjective, nonlinear complex optimization problem over basic CSA and particle swarm optimization, chicken swarm optimization and flower pollination algorithm is presented by illustrating various case studies on standard IEEE 14, 30 and 118–bus test systems.

Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price

The quantification of the uncertainty in crude oil price is of significance to improve the related financial decision-making. However, studies in this field have remained limited because the nonlinearity inherent in the crude oil price makes it challenging to model its uncertainty. In this paper, a novel learning system of ensemble probabilistic prediction combining five popular machine learning methods and an improved optimizer is presented to effectively model the uncertainty in crude oil price and establish the corresponding prediction interval with satisfactory reliability and resolution. An improved grey wolf optimizer based on the adaptive Cuckoo search algorithm (AGWOCS) is proposed in the learning system to integrate the prediction intervals produced by the above machine learning methods. In addition, the superiority of the proposed AGWOCS is validated based on an algorithm test, compared to three benchmark optimizers. To validate the effectiveness of the proposed learning system, the uncertainties in daily and weekly Europe Brent spot prices are modeled as a case study. The evaluation results based on the reliability, resolution, and sharpness demonstrate that the proposed learning system can yield the prediction interval with a higher quality, which has distinct advantages over eight benchmarks as a whole. The convergence and scalability of the learning system are also investigated, which reveals its feasibility.

An improved cuckoo search algorithm for multi-level gray-scale image thresholding

In decades, Yang’s cuckoo search algorithm has been widely developed to select the optimal threshold of bi-level image threshoding, but the amount of computation of which increases exponentially with multi-level thresholding. To reduce the computation quantity, the iterative step size is adaptively decided by its fitness values of the current iteration without using the Levy distribution in this study. The modification may cause the solution drops into the local optima during the later period. Therefore, the constant discovery probability pa is automatically changed relating to the current and total iterations. And then, to verify segmentation accuracy and efficiency of the proposed method, an adaptive cuckoo search algorithm proposed by Naik and Yang’s cuckoo search algorithm are included to test on several gray-scale images. The results show that the proposed algorithm is expert in selecting optimal thresholds for segmenting gray-scale image.

An extrinsic calibration method for multiple RGB-D cameras in a limited field of view

A 3D scanning system involving multiple RGB-D cameras has the potential to accelerate the reconstruction of an object and improve the measurement accuracy because it can capture an object in a comprehensive way. However, the extrinsic calibration of a multi-RGB-D-camera system is a fundamental and challenging problem, especially in a limited field of view. In this work, a system with four SR300 cameras on a platform with a size of approximately 0.64 m2 and an extrinsic calibration method assisted by a tower calibration pattern with circular markers in a limited field of view are proposed. The Hough transform algorithm is used to identify the centres of the circular markers in a colour image, and then the 3D coordinates are extracted by employing the alignment relationship between the colour image and the depth image. An improved adaptive cuckoo search (IACS) algorithm and unit quaternion are proposed to optimize the extrinsic parameters. The effectiveness of the IACS is verified by a comparison with the singular value decomposition (SVD), standard cuckoo search (CS) and two CS variant algorithms. In addition, the accuracy of the proposed extrinsic calibration method is verified by reconstructing and measuring a human foot and a cube box. The mean errors from the standard values of the length and width of the reconstructed foot mode are 0.18 cm and 0.13 cm, and the diameter of the yellow sphere are 0.17 cm, respectively. All of the experimental results show that the proposed extrinsic calibration method has a high calibration accuracy and good potential application prospects.

Metaheuristic Approaches for One-Dimensional Bin Packing Problem: A Comparative Performance Study

Nature-inspired metaheuristic algorithms have steadily gained popularity over the last two decades. They have been applied to a plethora of optimization problems both in continuous and combinatorial domains. In this paper, the one-dimensional bin packing problem is solved through the implementation of two underlying heuristics, namely, best fit and better fit, and four representative state-of-the-art global metaheuristic algorithms, namely, firefly algorithm, genetic algorithm, adaptive cuckoo search algorithm, and artificial bee colony algorithm. The underlying best fit and better fit heuristics are employed by the four aforementioned global metaheuristic algorithms as reordering heuristics and local search improvement mechanisms are used for generating good packing schema. Furthermore, these two local heuristics possess special characteristics which, when incorporated into the global metaheuristics, allow them to escape local optimums and avoid getting stuck, and more so, enables the algorithms to generate good quality solutions. The main focus of this paper is the presentation of a systematic performance evaluation study for the representative algorithms, with some initial computational results that show the effectiveness of the respective algorithms and their ability to achieve promising solutions. The experiments conducted here were carried out using three standard bin packing problem datasets categories with over 1,210 instances in total, each with differing capacities, number of items and distributions between item weights. The numerical results of the representative algorithms were compared with the solutions achieved with the underlying heuristic techniques, in this case, the best fit and better fit heuristics, respectively. Similarly, the analysis of the initial computational results obtained revealed the superior performance of the individual algorithm implementation. Moreover, performance was established by taking into account both the algorithms computational time and the solution quality. Overall, several observations regarding the solution and the underlying heuristics were made. It is worth noting that by utilizing best fit heuristic, the algorithms attained optimal solutions for instances of the easy dataset requiring smaller capacity bins. However, as the complexity of the instances increased, the ability to produce high-quality results decreased. Nevertheless, this heuristic can produce near-optimal solutions and a good packing schema for most instances. On the other hand, utilizing better fit as the underlying heuristic results in optimal solutions almost all the time, regardless of capacity and number of items.

An improved adaptive cuckoo search algorithm based on the population feature and iteration information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Lévy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

A Improved Adaptive Cuckoo Search Algorithm Based the Population Feature and Iteration Information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm

Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.

An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks

The evolution of wireless body area networks (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and connectivity. The placement of relay nodes in a wireless body area network (WBAN) plays an important role in design of energy efficient and reliable WBAN. This problem is a joint problem of data routing and placement of relay nodes and formulated as a linear integer programming model. The main objective of the problem is to minimize the cost of relay nodes, energy consumption and distributing the loads uniformly on the relay nodes. Considering the hardness of the problem, we propose an adaptive cuckoo search based algorithm which uses an efficient fitness function and an adaptive step size proportional to the fitness function for placement of relay nodes. The set of relay nodes obtained by our proposed adaptive cuckoo search algorithm compared with cuckoo search as well as other state of the art algorithms via simulation results. The simulation results reveal that the proposed algorithm not only consumes less energy than its counterparts but also distributes the load evenly on the relay nodes. We consider two different postures of the body with 13 biosensors placed in fixed positions and 50–100 candidate sites for placement of relay nodes. Furthermore, we also consider 80 biosensors randomly deployed in a rectangular area with 50–300 candidate sites to study the scalability of our algorithm.

An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm

In this study, the workspace volume of a 3R manipulator has been maximized using a biologically inspired optimization algorithm, namely Adaptive Cuckoo Search (ACS) algorithm. The proposed algorithm is tested on four diverse cases involving different constraints and without imposing any constraint. The outcomes of this study are compared with the standard results of different heuristics, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), and Cuckoo Search (CS) algorithm. Statistical tests were performed to test the superiority of the proposed algorithm. Further, the cross-sectional area of the voids in the workspace has been estimated. The convergence study along with the coefficient of variation analysis identifies the critical kinematic parameters. A constraint conformation study has been performed to investigate the relative importance of the constraints. In addition, to ensure the applicability of the proposed algorithm in practice it is tested by using the kinematic parameters of two existing industrial robot manipulators (KUKA KR-30 and Mitsubishi MRP-700A). It has been found that the predicted results from the proposed algorithm for the two robot manipulators are in line with the actual values.

Optimized vessel detection in marine environment using hybrid adaptive cuckoo search algorithm

Detection of vessels in a marine environment is a challenging task due to the complexity of identifying small objects, necessitating a detection algorithm to discriminate between variants in vessel-based geometry. False detection can be an issue due to sea clutter. The huge neural network-based computation models used for data analysis produce results that reflect the changing environment, based on iterations and common computation assumptions. To obtain the best results, the proposed research uses an optimization model with a classifier. This experimental model provides better accuracy than other detection systems, even with thousands of vessel images. This hybrid, adaptive cuckoo search-based optimization model produces the best results in dynamic sea-clutter regions, and outputs show lower false alarms in ports and other coastal surveillance regions.

A novel cuckoo search algorithm under adaptive parameter control for global numerical optimization

Cuckoo search (CS) is a well-known population-based stochastic search technique for solving global numerical optimization problems. At each iteration process, CS searches for new solutions by Levy flights random walk together with a local random walk (LRW). For LRW, mutation proceeds with a uniformly distributed random number in the interval [0, 1] as its mutation factor, which plays an important role in controlling the population diversity and the explorative power of the algorithm. However, this mutation factor generally results in sensitivity to the given optimization problem and thus fails to balance well these two aspects. In view of this consideration, we introduce a simple adaptive parameter control mechanism to LRW, and propose a novel adaptive cuckoo search (CSAPC) algorithm in this paper to improve the optimization performance of CS. The adaptive parameter control mechanism dynamically updates the control parameters based on a Cauchy distribution and the Lehmer mean during the iteration. To verify the performance of CSAPC, simulations and comparisons are conducted on 48 benchmark functions from two well-known test suites. In order to further test its efficacy, CSAPC is applied to solve the problem of parameter estimation of two typical uncertain fractional-order chaotic systems. The numerical, statistical and graphical analysis demonstrates the great competency of CSAPC, and hence can be regarded as an efficient and promising tool for solving the real-world complex optimization problems besides the benchmark problems.

Adaptive Cuckoo Search and Squirrel Search Algorithm for Optimal Cluster Head Selection in WSN

Adaptive Cuckoo Search and Squirrel Search Algorithm for Optimal Cluster Head Selection in WSN

Biologically Inspired Low Energy Clustering for Large Scale Wireless Sensor Networks

The recent technological advances in data gathering, embedded micro-devices and mobile networking has significantly advanced the applications of small-size and numerous tiny nodes. Large scale wireless sensor networks (LSWSNs) are intensively studied and used in the fields of traffic avoidance, intelligent family, medical diagnostic, environmental, multimedia surveillance, military affairs and so on. The recent success of emerging LSWSNs technology has encouraged researchers to develop new low energy clustering algorithm in this field. In LSWSNs, reducing communication energy consumption of sensor will not lead to maximize network lifetime for the total system. The low energy clustering is a typical NP-hard combinatorial optimization problem. In this paper, an immune adaptive cuckoo search algorithm (IACSA) is given to reduce total energy consumption. We first design a fitness function to evaluate energy consumption of system. The IACSA is designed to improve the energy efficiency for LSWSNs. It has the advantages of immune generator that takes into account different benefits and adaptive operator to enhance the convergence rate. Simulations are conducted to show a comparison of IACSA with the shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA). Results show that the proposed IACSA has lower energy consumption compared to the SFLA, AFSA and PSO, which means that the proposed method reduces the energy consumption.

Differentiation of Homogenous and Speckled Objects in HEp-2 Specimen Images using Keypoint Features and an Adaptive Cuckoo Search Algorithm

In this work, an attempt has been made to differentiate human epithelial type-2 (HEp-2) specimen images using Bag of Features (BoF) and Adaptive Cuckoo Search (ACS) feature selection. For this, 420 images consisting of homogenous and speckled patterns were obtained from a publicly available International Conference on Pattern Recognition (ICPR) 2016 database. These images are preprocessed using edge-aware local contrast enhancement and subjected to a speededup robust feature (SURF) descriptor for feature extraction. The optimal features are identified using the ACS method and are then fed into a support vector machine (SVM) for classification. The results show that the proposed approach is able to distinguish homogenous and speckled patterns. It is found that the features identified using ACS-based feature selection are significant. The proposed approach yields an average accuracy of 97.90% using the SVM classifier. Because automated analysis and classification of HEp-2 specimen images is important for the diagnosis of autoimmune diseases, this study seems to be clinically relevant.

Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping Algorithm

This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

In this paper, heuristic optimization techniques, such as integrated particle swarm optimization (IPSO), teaching--learning-based optimization (TLBO), and Jaya optimization, were applied effectively for the first time to optimize the radial distribution network (RDN) by simultaneously considering reconfiguration of the network and allocation and sizing of the distributed generations (DG). The objectives were to maximize the voltage stability and to minimize the power loss of the network without violating the system constraints. In standard PSO technique, the movement of current particle depends upon global best position and its own best position up to current step. However, if the particle lies too close to any of these positions, the guiding role highly decreases and even vanishes. To resolve this problem and to find the global best solution, IPSO was utilized to optimize the network reconfiguration and DG allocation and sizing problem in the RDN. Also, the optimization techniques, such as TLBO and Jaya optimization, which do not require any tuning of parameters, unlike other heuristic optimization techniques, were implemented successfully in this paper. Seven test cases were generated from different combinations of network reconfiguration and DG allocation and sizing. Moreover, for comparison, the optimization techniques, such as particle swarm optimization (PSO), adaptive cuckoo search algorithm (ACSA), harmony search algorithm (HSA), and fireworks algorithm (FWA), were also applied to IEEE 33- and 69-bus distribution test networks. The comparison results prove overall superiority of Jaya optimization when applied on the two IEEE bus systems with seven test cases undertaken.

A fuzzy adaptive controller for cuckoo search algorithm in active suspension system

This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the traditional genetic algorithm will not only have a premature convergence, but may also be trapped in the local optima. In the proposed fuzzy adaptive cuckoo search, all parameters of the PID controller and fuzzy rules are real coded to 75 bits in the optimization problem. Moreover, a fuzzy adaptive strategy is proposed for dynamically adjusting the learning parameters in the fuzzy adaptive cuckoo search, and this indeed enables global convergence. Experimental results verify that the proposed fuzzy adaptive cuckoo search algorithm can shorten the computing time in the evolution process and increase accuracy in the multiobjective optimization problem.

Multispectral Satellite Image Denoising via Adaptive Cuckoo Search-Based Wiener Filter

Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

Time‐frequency analysis and classification of power signals using adaptive cuckoo search algorithm

AbstractA new approach to Hilbert energy spectrum and pattern recognition of nonstationary power signals is presented in this paper. In the proposed work, visual localization, detection, and classification of nonstationary power signals are achieved using Hilbert transform (HT)–based adaptive local iterative filter (ALIF). The HT is applied on all the intrinsic mode functions that are obtained from both empirical mode decomposition (EMD) and ALIF to extract instantaneous amplitude and frequency components. The instantaneous Hilbert energy spectrum results in clear visual detection, localization, and classification of the different power signal disturbances. The visual energy spectrum by Hilbert‐Huang Transform (HHT) on ALIF showing a better result than HHT applied on EMD. The feature vectors are extracted from the Hilbert energy spectrum for automatic pattern recognition of various nonstationary signals using a traditional fuzzy C‐means algorithm (FCMA). Finally, the center of the cluster is further optimized using fuzzy C‐means–based adaptive cuckoo search algorithm. The average classification accuracy of the disturbances is 91.25% and 99.25% using fuzzy C‐means and adaptive cuckoo search–based FCMA, respectively.