Cuckoo Search Research Articles

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Performance Analysis of Metaheuristic Algorithms on Benchmark Functions

The discipline of optimization can be used to maximize or minimize several problems. The use of metaheuristic algorithms is a strategy that often works well for global optimization. They are a type of stochastic algorithm that, via trial and error, finds workable solutions to difficult optimization problems in a reasonable amount of time, but they do not provide assurance that the answers are optimal. This paper aims to offer a comparative analysis of several metaheuristics in searching for the optimal solution. The selected metaheuristics are Artificial Bee Colony, Ant Lion Optimizer, Bat, Black Hole, Cuckoo Search, Cat Swarm Optimization, Dragonfly, Differential Evolution, Firefly, Genetic, Gravitational-Based Search, Grasshopper Optimization, Grey Wolf Optimizer, Harmony Search, Krill-Herd, Moth-Flame Optimizer, Particle Swarm Optimization, Sine Cosine, Shuffled Frog-Leaping, and Whale Optimization algorithms. For this evaluation, 18 benchmark test functions, categorized as unimodal, multimodal, and fixed-dimension multimodal are used to examine various properties, such as accuracy, escape from the local optimum, and convergence. As an indicator of how effectively these metaheuristics work, metrics like minimum, maximum, average, and standard deviation of fitness are provided. There are no optimization algorithms that are adequate for all problems, as the No Free Lunch theorem suggests, but the metaheuristics that are more effective than the others will be demonstrated. This study could be helpful for young researchers to identify the most prominent metaheuristics for achieving a better global optimum. Received: 8 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Optimization, Metaheuristic algorithm, Benchmark function, Performance metric

A novel development of h-CSLQFL controller for optimal sizing and economic pricing of battery in electric vehicle system

This paper proposes a novel design of h-CSLQFL controller for reducing the battery size (kWh) and battery cost (US$/kWh) of electric vehicle (EV) system. The h-CSLQFL controller is a hybrid controller which is formed by the combination of Cuckoo search optimization (CSO), linear quadratic regulator (LQR), and fuzzy logic controller (FLC). The higher battery cost is one of the biggest challenges with EV due to which a significant number of consumers cannot afford to purchase it. The replacement of conventional vehicles with EV creates ecofriendly and sustainable solutions. For attaining the optimal reduced cost of battery, a strategy has been demonstrated by framing a multi-objective function which is controlled with h-CSLQFL controller. It commences with the design of mathematical model of EV in which supply voltage and current are the input parameters whereas the speed and torque are the output parameters. Thereafter, LQR controller was designed and implemented for analyzing the performance parameter of EV. Afterwards, CSO controller in support with LQR controller has been formulated in closed loop manner. At the end, the multi-objective function is controlled with LQR based CSO and LQR controller. It is observed that the least optimal economic cost and size of EV battery have been attained with h-CSLQFL controller in comparison to the LQR based CSO and LQR controller. In addition to this, the least risk factor and reduced sensitivity have been also attained with the proposed approach. The validation of the proposed h-CSLQFL controller has been done on a hardware set up.

FMCSSE: fuzzy modified cuckoo search with spatial exploration for biomedical image segmentation

FMCSSE: fuzzy modified cuckoo search with spatial exploration for biomedical image segmentation

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.

An efficient spider wasp optimizer-based tracker for enhancing the harvested power from thermoelectric generation sources

The distribution of heat at the cold and hot sides of a thermoelectric generator (TEG) is essential to its operation. Due to the inhomogeneous pattern, the TEG power-voltage (P–V) curve exhibits multiple peaks, which pose significant consequences for TEG generation. In order to increase the system conversion efficiency at this point, tracking global peak power (GPP) using a maximum power point tracker (MPPT) is crucial. The peak generation from TEG devices cannot be harvested by traditional algorithms such as hill climbing (HC) because they fall in local peaks. Therefore, the spider wasp optimizer (SWO), a new metaheristic-based tracker, is proposed in this paper to monitor the global power from TEG system operating at inhomogeneous heat distribution. In SWO, the iterative process of changing the population length aids in avoiding local solutions. The suggested SWO-based tracker is responsible for regulating the dc-dc boost converter linked to the TEG array terminals by giving its MOSFET the proper duty cycle. Seven inhomogeneous heat distributions are examined for two TEG arrays: symmetric 9 × 9 and asymmetric 10 × 15. These heat distributions are uneven row, uneven column, diagonal, outer, center, and random. The proposed approach is compared to artificial gorilla troops optimizer (GTO), gold rush optimizer (GRO), walrus optimization algorithm (WaOA), particle swarm optimizer (PSO), chef-based optimization approach (CBOA), cuckoo search (CS), Jellyfish search approach (JS), seagull optimization approach (SOA), and sine cosine approach (SCA). With regard to 9 × 9 array, the suggested SWO-tracker performed better than the published GTO method since it increased the generated power in the following scenarios: uneven row, uneven column, SN, diagonal, outer, center, and random patterns by 1.225 W, 0.4083 W, 19.5838 W, 2.4596 W, 1.394 W, and 2.4463 W, respectively. In asymmetric array, the suggested SWO performed better than all others, obtaining the least errors in relation to the Simulink GPPs of 0.016 W, 0.0822 W, 0.3615 W, 0.0603 W, 0.0569 W, 0.0081 W, and 0.0089 W. The obtained outcomes validated the proficiency and dependability of the suggested SWO-tracker in all examined cases.

Damage detection method for square steel tube based on CS-NME algorithm via ultrasonic guided waves

The utilization of ultrasonic guided wave (UGW) technology has garnered considerable attention in non-destructive testing field, particularly for steel components. However, for square steel tubes with noncircular cross sections, this method encounters severer frequency dispersion, which hampers the accuracy of damage identification. To address this issue, the approach utilizing the normal mode expansion (NME) method combined with cuckoo search (CS) algorithm to develop a layout strategy for ultrasonic transducer position and length is proposed, which enables single mode excitation of UGW while reducing the interference from redundant clutter signals, thereby enhancing the accuracy of damage detection. The efficacy of the proposed approach is evaluated via numerical simulations and laboratory experiments. The results show that this method can significantly reduce the interference of other modes by optimizing the location and length of ultrasonic transducers, and successfully detect the hole and crack damage on the square steel tube.

Hybrid Optimal Power System Recovery and Load Pick-Up With DGAllocation In Electrical Power System

The increasing demand for electricity, the expansion of complex power networks, and the operation of power systems near their capacity have heightened the risk of power outages. Consequently, control room operators must develop comprehensive restoration plans to minimize recovery time. Key steps in power system restoration include starting non-black-start units with black-start units and initiating load pick-up. This process requires identifying the shortest route between nodes to expedite repairs. This research presents an expert plan employing the Modified Cuckoo Search algorithm (MCSA) to determine optimal pathways. Additionally, it explores the optimal allocation of distributed generators (DG) to enhance the system's voltage profile and reduce power losses using particle swarm optimization (PSO). The results demonstrate that DG significantly reduces losses during recovery. The study prioritizes the restoration of DGs with strong black start capability, considering network and DG constraints. The effectiveness of the proposed restoration approach is validated through MATLAB simulations on theIEEE 39-bus system.

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Retraction Note: An Improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations

Retraction Note: An Improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations

Optimized integration of renewable energy sources using seven-level converter controlled by ANFIS-CS-GWO

The global energy landscape is undergoing a significant transformation, driven by the need to reduce greenhouse gas emissions, enhance energy security, and provide sustainable and clean power solutions. Renewable energy sources (RES) such as Solar Photovoltaic (SPV), wind, fuel cells, and tidal power play a crucial role in this transition due to their abundant availability and low environmental impact. However, integrating these diverse energy sources into power systems requires advanced control strategies to ensure stability, efficiency, and reliability. Existing systems, however, face significant challenges such as unstable voltage profiles, prolonged fault settling time (FST), and high total harmonic distortion (THD), which compromise their efficiency and reliability. To overcome these limitations, a novel methodology is proposed, integrating Solar PV (SPV), wind, fuel cell, and tidal energy sources into a seven-level converter (SLC) system, which is named as Four Level RES based SLC (FLRES-SLC) system. This system is managed by an Adaptive Neuro-Fuzzy Inference System (ANFIS), fine-tuned by Cuckoo Search adopted Grey Wolf Optimizer (CS-GWO). This hybrid control strategy ensures a robust voltage profile, significantly reduces FST, and lowers THD, thereby markedly improving the overall performance of the converter system. The innovative approach offers a reliable and efficient solution for modern power systems, enhancing their integration with diverse RES and ensuring high-quality power delivery.

Lithium-ion battery parameter estimation based on variational and logistic map cuckoo search algorithm

Lithium-ion battery parameter estimation based on variational and logistic map cuckoo search algorithm

A Novel Dung Beetle Optimization Approach for Automatic CMOS Analog Circuit Design

The main objective of the automated circuit sizing technique is to deal with the challenges of design tradeoffs in analog circuit design with improved accuracy and efficacy. Analog circuit design represents a multi-objective optimization challenge, where designers must properly balance various performance factors such as input and output impedance, power dissipation, area, unity-gain bandwidth, slew rate, and open-loop DC gain. Traditional design equations provide a sizing of differential amplifier MOS transistors, further optimization can be achieved through the application of meta-heuristic search techniques. Meta-heuristic search techniques can be used as local optimizers in a smaller search area to improve the optimization of design parameters. The differential amplifier circuit with current mirror load is optimized through the application of the Dung Beetle Optimization (DBO) algorithm. By using an evolutionary algorithm called DBO, all the required parameters were achieved with the least amount of transistor area and power dissipation when compared to the results of the Seeker Optimization Algorithm (SOA), Opposition based Harmony Search Algorithm (OHS), craziness-based particle swarm optimization (CRPSO), and Cuckoo Search (CS) algorithms.

MRI Brain Tumor Segmentation using Cuckoo-based Dimensionality Reduction and Ensemble Convolutional Neural Network

Background Brain tumor identification at an early stage is a challenging task that increases the lifetime of patients. Specialists' conclusions on recognizing brain tumors are difficult, as they are based on their theoretical knowledge. It takes a huge amount of time to diagnose the patient. Recently, research has suggested an automated technique that is dependent on convolutional neural networks. Medical pictures are a set of accumulations of data that are hard to store and process, expending broad registering time. The decreased infiltrated systems are normally utilized as an information pre-preparing venture to make the picture information less mind-boggling with the goal that high-dimensional information may be recognized by a fitting and apt low-dimensional portrayal. Objective This study proposes an optimization-based dimensionality reduction and brain tumor segmentation using ensemble convolutional neural networks in MRI images to enhance disease diagnosis and extend healthcare accessibility. Methods Cuckoo-based dimensionality reduction and Ensemble CNN are proposed to segment the tumor region . The cuckoo-based optimization search technique is used to reduce the dimensionality of MRI Brain Images to perform better segmentation. The proposed technique is evaluated on the BRATS database, which contains two datasets: the Leaderboard and Challenge datasets. The outcomes are estimated utilizing the Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity. Results The Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset. The proposed method outperformed the leaderboard dataset with a greater 91% Dice Similarity Coefficient (DCE), 95% Positive Predictive Value, and 87% Sensitivity of High-Grade Glioma (HGG). Seventy-two percent Dice Similarity Coefficient (DCE), 70% Positive Predictive Value, and 93% Sensitivity of Low-Grade Glioma (LGG). 88% Dice Similarity Coefficient (DCE), 90% Positive Predictive Value, and 91% Sensitivity of combined High-Grade glioma and Low-Grade glioma. For the BRATS Challenge dataset, the proposed method provides a 92% Dice Similarity Coefficient (DCE), 93% Positive Predictive Value, and 95% Sensitivity of High-Grade Glioma (HGG). 86% Dice Similarity Coefficient (DCE), 88% Positive Predictive Value and 93% Sensitivity of Low-Grade glioma (LGG). 85% Dice Similarity Coefficient (DCE), 89% Positive Predictive Value, and 92% Sensitivity of combined High-Grade glioma and Low-Grade glioma. Conclusion In this study, MRI Brain tumor segmentation using Cuckoo-based dimensionality reduction and Ensemble Convolutional Neural Network is proposed. The cuckoo search algorithm used for dimensionality reduction is performed in MRI images to reduce the dimensions. We also compared two of the existing methods with our proposed method. The leaderboard dataset and challenge dataset have been discussed. The challenge dataset for HGG provided good results in terms of dice similarity coefficient and positive predictive value. The sensitivity alone gets reduced when compared with the CNN and random forest methods. Experimental analysis shows promising results on the leaderboard dataset and the BRATS Challenge dataset.

Improving photovoltaic cell parameter calculations through a puffer fish inspired optimization technique

The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.

An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimization (Harris hawks optimization (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback.

Enhancing Classification Algorithms with Metaheuristic Technique

Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. However, the classification algorithm cannotclassify data optimally due to the challenges in dealing with variousdata sets. Not all available featureswillmake a solidcontribution to the label of the data class, often in the form of noise or interference. For this reason, it is necessary to carry out a feature selection process. Currently, many feature selection processes have been carried out using correlation values from chi-square and gain-information, but the accuracy of the resultsis often still not good enough. This is because the chi-square and gain-information values are fixed. So,the selection of features is minimaland is not based on the previous learning process or what is known as heuristics. For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. In its operation, the metaheuristic algorithm optimizes the feature selection process,which will later be processed using the classification algorithm.Three (3) meta-heuristics were implemented, namely Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm; the experiment was conducted, and the results were collected and analyzed. The result shows that combining Naive Bayes and Genetic Algorithmgives the best performance regarding higher accuracy improvementat +23.77%.

Optimization of the Offshore Wind Turbines Layout Using Cuckoo Search Algorithm

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA.

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.