Shuffled Frog Leaping Algorithm Research Articles

Early detection of chronic kidney disease using eurygasters optimization algorithm with ensemble deep learning approach

Chronic kidney disease (CKD) emerges as a global health problem with high morbidity and mortality rates, and it induces other diseases. Patients often fail to notice these diseases because there are no obvious symptoms during the earlier stage of CKD. Earlier diagnosis of CKD allows them to receive early medical intervention to improve the disease progression and outcome. Earlier stratification of CKD can affect the health care provided to patients through various options, such as kidney transportation, hemodialysis, or pharmacological care in milder cases. Recently, deep learning (DL) and machine learning (ML) approaches have gained significance in the domain of medical diagnoses due to their high prediction accuracy. DL and ML approaches can successfully aid physicians in achieving these goals due to their accurate and fast detection performance. The performance of the proposed technique mainly depends on selecting the suitable algorithms and appropriate features. Thus, the study introduces an Eurygasters Optimization Algorithm with an Ensemble DL CKD detection (EOAEDL-CKDD) method. The EOAEDL-CKDD method aims to detect and classify the presence of CKD using feature selection and hyperparameter tuning strategies. The EOAEDL-CKDD method applies a min-max scalar to convert the data into a uniform format to achieve this. The EOAEDL-CKDD technique exploits EOA to select features. This is followed by an ensemble of long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and bidirectional LSTM (BiLSTM) models that are used for the CKD detection process. Finally, a shuffled frog leap algorithm (SFLA) based hyperparameter selection process is carried out to choose the ensemble models' hyperparameter values optimally. The empirical assessment of the EOAEDL-CKDD method is tested on the benchmark CKD dataset. The experimental values highlighted that the EOAEDL-CKDD technique gains an optimal detection rate compared to existing models.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Wind power optimization using modified memetic algorithm

This research paper introduces an approach for optimizing the design of wind turbines to enhance their electrical output. To efficiently convert wind energy into electrical energy, a specific turbine design is essential. The study employs a modified shuffled frog-leaping algorithm (MSFLA), an improved variant of the memetic algorithm, to determine the optimal wind power coefficient by adjusting rotor speed at various wind velocities (including rated, cut-in, and cut-out speeds). MSFLA combines global and local search techniques, following divide-and-conquer principles, to achieve better results. By imposing specific turbine constraints, the simulation demonstrates that the wind power coefficient can be increased under optimized conditions.

IoT service composition based on improved Shuffled Frog Leaping Algorithm

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

An adaptive shuffled frog-leaping algorithm for parallel batch processing machines scheduling with machine eligibility in fabric dyeing process

Fabric dyeing is an important production process in clothing industry and consists of some batch processing machines (BPM). In this study, parallel BPM scheduling problem with machine eligibility in fabric dyeing process is considered and an adaptive shuffled frog-leaping algorithm (ASFLA) is presented to minimise makespan and total tardiness simultaneously. A heuristic is used to produce initial population. Evolution quality of population on the last generation is used to implement adaptive population division and adaptive search process in each memeplex on the current generation. Search process of each memeplex is divided into some segments and search operators are dynamically adjusted after each segment is done. Adaptive elimination is performed on the worst memeplex. Extensive experiments are conducted to test the performance of ASFLA and the computational results demonstrate that new strategies of ASFLA are effective and ASFLA has promising advantages on the considered problem.

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content.

A modified shuffled frog leaping algorithm with inertia weight

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively.

Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use.

Non-Invasive Plant Root Tomography Through Optimized Sonar Array Transducer Antenna Design Using Genetic Swarm Metaheuristic

Plant root imaging is crucial for progress in various domains such as plant breeding and crop optimization. Traditionally, root tomography involves invasive methods that disrupt plant systems and yield non-reproducible results. As a result, non-invasive techniques, particularly electrical tomography, have gained significant attention. Despite the advantages, these techniques have limitations in terms of radiation efficiency and directivity due to suboptimal antenna design. This paper presents a comprehensive simulation on antenna design optimization focusing on dimensions, spacing, and integration of advanced algorithms. A micropatch transducer antenna was engineered for an existing in-silico plant root setup operating within a 3–5 MHz frequency range. The optimized dimensions of the antenna are 109.32 mm × 140.67 mm × 2.55 mm, and it resonates effectively within a frequency range of 3.1–5.68 MHz. Using scalar minimization techniques, patch transducers were interconnected into an antenna array with an optimized 3 mm spacing. Utilizing multi-objective optimization algorithm based on sperm fertilization procedure and shuffled frog leaping algorithm, optimal frequencies were obtained at 3,989,796.88 Hz and 3,989,951.83 Hz, respectively. Validated using CADFEKO software, the proposed antenna design demonstrated distinctive voltage distribution, superior directivity of 9.24 dBi, gain of 9.15 dBi, and 98.6% radiation efficiency when compared to the existing silicon-based root tomography antenna setups.

Implementation of improved shuffled frog leaping algorithm for optimum landscape space environment design

Abstract Landscape design is a complex process, requiring the seamless integration of various elements such as the senses, environment, morphology and optimization techniques. In this study, a new approach called the Interactive Enhanced Shuffled Frog Leaping Algorithm (IISFLA), specifically has been designed to optimize spatial environments in landscape design. The IISFLA is a hybrid optimization method that combines the Shuffled Frog Leaping Algorithm (SFLA), Bacterial Foraging Algorithm and human–computer interaction to address intricate nonlinear challenges. To demonstrate its effectiveness, IISFLA has been applied in the design of a garden community and contrasted with conventional design of landscape methodologies. To tackle the problem at hand, a optimality model of search that is layer-by-layer, which allows us to divide the problem space into manageable subsolution spaces, has been employed. Also, a participatory evaluation strategy has been adopted to assess the impact of the landscape design on different user groups. The results of this study reveal that IISFLA surpasses conventional methods in terms of performance and user satisfaction, making it a versatile and user-friendly tool for landscape designers. The research emphasizes the innovation, significance and implications of our work in the field of landscape design and optimization. Key numerical findings include a 22.6% increase in landscape space evaluation value when using IISFLA compared to traditional methods, a 34.7% reduction in computational time and a 28.9% improvement in user satisfaction rates. These outcomes highlight the effectiveness and potential of IISFLA in enhancing landscape design outcomes.

Multi-objective shuffled frog leaping algorithm for deployment of sensors in target based wireless sensor networks

In this research, –coverage –connected problem is viewed as multi-objective problem and shuffling frog leaps algorithm is proposed to address multi-objective optimization issues. The shuffled frog leaping set of rules is a metaheuristic algorithm that mimics the behavior of frogs. Shuffled frog leaping algorithms are widely used to seek global optimal solutions by executing the guided heuristic on the given solution space. The basis for the success of this SFL algorithm is the ability to exchange information among a group of individuals which phenomenally explores the search space. SFL improves the overall lifespan of the network, the cost of connection among the sensors, to enhance the equality of coverage among the sensors and targets, reduced sensor count for increased coverage, etc. When it comes to coverage connectivity issues, each target has to be covered using k sensors to avoid the loss of data and m sensors connected enhance the lifespan of the network. When the targets are covered by k sensors then the loss of data will be reduced to an extended manner. When the sensors are connected with m other sensors then the connectivity among the sensors will not go missing and hence the lifespan of the network will be improved significantly. Therefore, the sensor node number in coverage indicates the total number of sensor nodes utilised to cover a target, and the number of sensor nodes in connected reflects the total number of sensor nodes that provide redundancy for a single failed sensor node. Connectivity between sensor nodes is crucial to the network’s longevity. The entire network backbone acts strategically when all the sensors are connected with one or the other to pertain to the connectivity of the network. Coverage is yet another key issue regarding the loss of data. The proposed algorithm solves the connectivity of sensors and coverage of targets problems without weighted sum approach. The proposed algorithm is evaluated and tested under different scenarios to show the significance of the proposed algorithm.

Shuffled frog Leaping algorithm based circular patterns loaded ring shaped fractal antenna for multistandard wireless applications

This paper describes the design and experimental behavior of a 2nd iterated modified Square Ring Shaped Fractal Antenna (SRSFA). The radiator geometry consists of three symmetrical square-shaped rings loaded with circular patterns at the corners. The designed fractal patch is placed on a slot loaded ground structure. The electrical dimensions possessed by the realized structure are 0.15λ0 x 0.15λ0 x 0.080 λ0, and the whole antenna is implemented using FR4 substrate. In this design, transmission line width ‘WF’ and position ‘FP’ are optimized using Shuffled Frog Leaping Algorithm (SFLA) and Artificial Bee Colony (ABC) strategies. It has been examined that the optimized values of ‘WF’ and ‘FP’ using SFLA and ABC are 2.6 and 12.4 mm, and 3.2 and 15.9 mm, respectively. Results reveal that SFLA offers better solution quality and convergence speed than ABC. Further, experimental analysis has been demonstrated to effectively illustrate the recommended fractal approach. The equivalent circuit of the designed structure is implemented successfully. The conducted measurements show that for VSWR ≤ 2, the fabricated structure produces six resonance points with adorable S11 values. The resonances appear at 1.53 GHz (1.35–1.67 GHz), 4.30 GHz (4.20–4.40 GHz), 7.58 GHz (7.43–7.70 GHz), 11.35 GHz (10.37–11.58 GHz), 14.03 GHz (13.85–14.35 GHz), and 15.93 GHz (15.43–16.25 GHz). The examined −10 dB impedance bandwidth at the respective resonance points is 20.9 %, 4.6 %, 3.5 %, 11.3 %, 2.9 %, and 5.23 %. Healthy concordance is noticed among the experimental measurements and electromagnetic simulations. The demonstrated radiation patterns are arbitrary bidirectional/omnidirectional at the respective resonances with good stability. Additionally, the offered gain is within the range of 4.91–7.00 dB. Hence, by utilizing the above-mentioned approaches, miniaturization of 65 % has been achieved. Practical outcomes suggest that the introduced prototype exhibiting multiband behavior, can be effectively applied in Radio location, Radio astronomy, Long-range tracking, Weather location Broadcasting, Search for extra-terrestrial intelligence, Radar altimeters, High resolution mapping, Aeronavigation, and Miscellaneous Radars.

PREDICTING THE STATUS OF THYROID AND CARDIOVASCULAR PATIENTS ACCORDING TO THEIR ELECTRONIC RECORDS USING TEMPORAL ELEMENTS BASED ON THE COMBINATION OF SHUFFLED FROG LEAPING ALGORITHM (SFLA) AND DEEP LEARNING

Health and treatment are two of the most important application fields of information technology, in which the problem of predicting a disease is highly important. The physician makes such predictions based on the clinical condition of the patient and the level of facilities and advances in medical knowledge for the patient—information technology benefits from multiple methods to help this field. Accordingly, the patient information storage system, drug information, treatment and surgery systems, treatment follow-up systems, remote treatment systems, etc., aim to facilitate the treatment process. The patient can receive the best services within the shortest time due to these systems and information availability. The doctor can provide services to his patient anywhere in the world. This paper provided a model to predict the condition of patients based on their electronic records using temporal elements based on combining the shuffled frog leaping algorithm (SFLA) and deep learning. Accordingly, the evolutionary shuffled frog leaping algorithm (SFLA) and deep learning were used for preprocessing, feature selection, and classification. Two datasets of cardiovascular and thyroid diseases were utilized in the simulation section to ensure the efficiency of the proposed method. Based on this simulation, the proposed method indicated improvement compared to similar methods in the evaluated datasets. In the cardiovascular diseases dataset, this improvement was recorded as 1.4% and 3.2% compared to the author's previous and updated similar methods, respectively.

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network’s resilience during critical periods is investigated, as well as improvements in the network’s technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem’s objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA–SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms.

Optimization methods for acoustic material selection in interior spaces

The quality of the acoustic environment required in a room varies with the application, which could include industrial spaces, office rooms, classrooms, theatres, etc. Treatment of surfaces in rooms with acoustic materials is important in achieving the desired acoustic quality. In an industrial environment, it is necessary to treat walls with interior and exterior acoustic linings that are both cost and acoustical quality efficient. This work intends to develop optimization strategies for acoustic material selection using cost minimization as the objective function, in conjunction with acoustical requirements and material selection limitations. The first technique is a genetic algorithm (GA) with dual stage variable chromosome, which improves the ease of constraint implementation in the algorithm. The second method is the Shuffled Frog Leaping Algorithm (SFLA) with an implementation over a discrete domain. A better convergence has been observed with SFLA over GA due to group improvisation with local and global best population. The described algorithms are demonstrated and compared for office and classroom acoustic treatments.

Forensic Characterization of Mobile Phone Cases Using Infrared Spectroscopy with Deep Learning Methods

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model’s performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science.

A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task

It can be extremely difficult to find the optimal solution in many complex optimization problems. The goal of optimization algorithms in such cases is to locate a feasible solution that is as close as possible to the optimal one. These algorithms are called metaheuristic optimization algorithms and the majority of them take their inspiration from nature and work to solve challenging problems in a variety of fields. In this paper, a combination between GWO and Coot algorithm was proposed. The effectiveness of the GWO algorithm has been demonstrated in many fields, including engineering and medicine. However, GWO has a disadvantage: the potential to enter the local minima due to a lack of diversity. GWO and the Coot algorithm were merged to fix this flaw. Ten benchmark functions were used to evaluate the performance of this hybrid technique, and its results were compared to those of other common optimization algorithms, including GWO, Cuckoo Search (CS), and the Shuffled Frog Leaping algorithm (SFLA). The results show that the suggested algorithm can provide results that are both competitive and more consistent than the other algorithms in most test functions.