Multi-objective Grasshopper Optimization Algorithm Research Articles

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Trajectory planning of tire laser engraving orthogonal robot based on an improved multi-objective grasshopper optimization algorithm

The traditional tire laser engraving robots have the low and stable running capability and discontinuous motion trajectory during operation, leading to long running time, high energy consumption, and uneven operation. In order to solve these problems, this paper proposes an orthogonal robot trajectory planning method based on an improved multi-objective grasshopper optimization algorithm (IMOGOA) with the orthogonal robot as the research object. Firstly, this paper used the quintic non-uniform rational b-splines (NURBS) curves to construct the trajectory. Secondly, the optimization objectives of the orthogonal robot’s running time, energy consumption, and smoothness were used to obtain the Pareto optimal solution by using the IMOGOA to establish the objective function. Then, the optimization method was simulated and analyzed, and the optimal solution was selected to compare with the rest of the non-dominated solution sets. The simulation results showed that the proposed method reduced the running time by 33.68%, improved the energy consumption and smoothness performance by 34.48% and 56.58%, and achieved multi-objective comprehensive optimization of the orthogonal robot. Thus, the effectiveness and feasibility of the proposed method were verified. Finally, a tire laser engraving orthogonal robot experimental platform was built for physical verification experiments. The experimental results showed that the experimental trajectory deviated from the simulated trajectory by only 5.63%, further proving the proposed method’s practicality.

Structural Optimization Design of Three Phase Enclosed GIS Disconnector Based on MOGOA and ELM

Due to the compact and complex internal structure of 110 kV three‐phase enclosed GIS disconnector, the voltage distribution between the contact breaks of each phase has an influence on the insulation performance of the GIS disconnector, and its internal insulation design is a difficult point to be solved. To solve this problem, this paper firstly establishes a three‐dimensional model of three‐phase enclosed GIS disconnecting switch and conducts a research on the distribution of electric field under a specific working condition, it is found that the outer corner of the static contact, the inner corner of the static arc contact, the inner corner of the outer end of the moving contact and the inner corner of the moving contact are the internal electric field concentration of the three‐phase enclosed GIS disconnector. Then, in order to further reduce the electric field strength and improve the insulation margin and performance, this paper proposes a method based on the combination of Extreme Learning Machine (ELM) and Multi‐objective Grasshopper Optimization Algorithm (MOGOA) to optimize the structural design of three‐phase enclosed GIS disconnector, and a ELM model is established, which takes the internal fillet radius of stationary arcing contact, the internal fillet radius of moving contact, the internal fillet radius of external end of dynamic and static contact, the radius of the outer corner of the two static side contacts and the opening distance of the grounding contact as the inputs, and takes the maximum electric field strength of the two static side contacts and the moving side contacts as outputs; Finally, the structural parameters are optimized by MOGOA algorithm. Compared to the pre‐optimization period, the electric field strengths of the bus bar static‐side contact, dynamic contact and static side contact of the B‐phase after optimization decreased from 20.2, 9.28 and 17.5 to 11.4, 6.65 and 11.5 kV/mm, respectively. The electric field strengths of the dynamic and static side contacts are significantly reduced, which is beneficial for improving the insulation performance of the three‐phase enclosed GIS disconnector. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers

The widespread deployment of data centers has contributed to a dramatic increase in energy consumption and carbon emissions. To solve this problem, this study proposes a distributed multi-energy system (DMES) driven by solar, wind, geothermal and natural gas and its life-cycle-based multi-objective optimization model considered energy, economy, and environment. Moreover, a novel operation strategy based on load characteristics is presented for energy flow allocation and a multi-objective grasshopper optimization algorithm is improved for model solving. Case studies are conducted on a practical data center in Qinghai, China. Based on the DMES capacity configuration and scheduling results, the cooling, heating and power supply can satisfy the demand of the data center while generating over 30% of the power from renewable sources. In addition, carbon tax is considered in the optimization model, which accounts for 4% of the life-cycle cost. Furthermore, the sensitivity of carbon tax, equipment lifespan and predicted mean vote is analyzed, obtaining that these factors have some influence on the performance of the system. Finally, the uncertainty impact on system planning is studied, concluding that the designed system exhibits high robustness levels when renewable energy forecast error is less than 20% or load forecast error is less than 10%.

Process design, integration, and optimization of a novel compressed air energy storage for the coproduction of electricity, cooling, and water

The use of fluctuating renewable energy over a certain threshold may lead to an unmanageable mismatch between the electricity generation and demand profiles threatening the grid's stability. In this study, an innovative complex energy storage/conversion system is proposed for the cogeneration of electricity, cooling, and water by integrating the liquefied natural gas (LNG) regasification process, an organic Rankine cycle, a compressed air energy storage (CAES) system, and a multi-effect distillation unit. The study attempts to minimize the CO2 emission from the CAES technology while addressing interruptions and reductions in the grid upon the extensive use of intermittent renewables. In addition, the proposed system uses excess power and waste heat during the charging and discharging of the CAES to regasify LNG and produce fresh water. The reference system performance is analyzed considering thermodynamic, economic, and environmental perspectives. The multi-objective grasshopper optimization algorithm is used to make a trade-off between the technical, economic, and environmental performance factors of the system. The results show an exergy efficiency of 50.6 % and a total cost rate of 322.8 $/h for the proposed system at the TOPSIS optimal point. The Grassmann diagram indicates the combustion chamber is the main source of irreversibility, and the Chord diagram revealed the discharge unit was responsible for more than 55 % of the total cost.

Grasshopper Optimization Algorithm for Multi-objective Optimization of Multi-pass Face Milling of Polyamide (PA6)

<div>Milling is a prevalent machining technique employed in various industries for the production of metallic and non-metallic components. This article focuses on the optimization of cutting parameters for polyamide (PA6) using carbide tools, utilizing a recently developed multi-objective, nature-inspired metaheuristic algorithm known as the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). This optimization process’s primary objectives are minimizing surface roughness and maximizing the material removal rate. By employing the MOGOA algorithm, the study demonstrates its efficacy in successfully optimizing the cutting parameters. This research’s findings highlight the MOGOA algorithm’s capability to effectively fine-tune cutting parameters during PA6 machining, leading to improved outcomes in terms of surface roughness reduction and enhanced material removal rate.</div>

Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit

Remaining useful life (RUL) prediction plays a crucial role in bearing health management which can guarantee the rotating machinery systems’ safety and reliability. This paper promotes the capability of bearing health management by proposing a novel data-driven prognostic approach. The proposed methodology reveals the underlying deterioration progression by constructing a new health indicator (HI) that fuses distribution-similarity-based multi-scale features. More specifically, a graph named WDgram, to represent the distribution similarity between the analyzed signal and healthy reference at different frequency scales, is developed first using the 1/3-binary tree structure and Wasserstein distance (WD). Then the high-dimensional multi-scale features are fused by the multiobjective grasshopper optimization algorithm (MOGOA) to optimize the HI's monotonicity, robustness, and tenability. By taking advantage of the WDgram and MOGOA, the novel HI is able to thoroughly reveal the underlying degradation characteristics and alleviate random fluctuations which frequently decrease the accuracy of RUL estimation. Subsequently, a gated recurrent unit network, incorporated with the grasshopper optimization algorithm (GOA) to adaptively determine some hyperparameters, is established with high predictive accuracy and generalizability. Experimental validation is conducted to prove the effectiveness of the proposed method. High accuracy and generalizability can be obtained by the proposed method, demonstrating its substantial advantages in health management for rotating machinery systems.

Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response

Due to the challenges associated with grid management, it is crucial to develop energy management systems that consider optimal resource performance within an integrated system and enable control of power exchange with the grid. Simultaneously, addressing the demand for multiple energy sources is a critical step towards achieving an economically and efficiently integrated energy system (IES), which plays a pivotal role in promoting sustainable IES development. Building upon these considerations, this article proposes a two-objective optimization model that incorporates the concept of demand response and integrated energy systems, while considering comprehensive economic and energy efficiency benefits. To ensure realism, a range of linear and nonlinear constraints are employed. To address such a problem, a two-stage modeling and solution approach is proposed. Initially, flexible electrical and thermal loads are combined to establish a mechanism for effectively responding to system behavior. Subsequently, a multi-objective model is presented to enhance energy generation and transfer efficiency, taking into account both demand response and economic aspects. Given the distinct properties of these objectives, a multi-objective grasshopper optimization algorithm (GOA) is developed. The Pareto concept is utilized to enhance search performance, and fuzzy theory is employed to select the optimal solution from the set of solutions. Finally, the proposed model and algorithm are evaluated under various operating conditions on the studied system. The results demonstrate that this model effectively improves economic aspects, enhances efficiency, and reduces pollutant emissions to an acceptable level.

An efficient hybrid approach for optimization using simulated annealing and grasshopper algorithm for IoT applications

The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi-objective optimization problems in IoT applications. In order to enhance its performance and improve global convergence speed, the algorithm integrates simulated annealing (SA). Simulated annealing is a metaheuristic algorithm that is commonly used to improve the search capability of optimization algorithms. In the case of MOGOA, simulated annealing is integrated by employing symmetric perturbation to control the movement of grasshoppers. This helps in effectively balancing exploration and exploitation, leading to better convergence and improved performance.The paper proposes two hybrid algorithms based on MOGOA, which utilize simulated annealing for solving multi-objective optimization problems. One of these hybrid algorithms combines chaotic maps with simulated annealing and MOGOA. The purpose of incorporating simulated annealing and chaotic maps is to address the issue of slow convergence and enhance exploitation by searching high-quality regions identified by MOGOA.Experimental evaluations were conducted on thirteen different benchmark functions to assess the performance of the proposed algorithms. The results demonstrated that the introduction of simulated annealing significantly improved the convergence of MOGOA. Specifically, the IDG (Inverse Distance Generational distance) values for benchmark functions ZDT1, ZDT2, and ZDT3 were smaller than the IDG values obtained by using MOGOA alone, indicating better performance in terms of convergence. Overall, the proposed algorithms exhibit promise in solving multi-objective optimization problems.

Improved multi-objective grasshopper optimization algorithm and application in capacity configuration of urban rail hybrid energy storage systems

Aiming at addressing problems of poor diversity and ergodicity of initial population, slow convergence speed, and susceptibility to local optima in conventional multi-objective grasshopper optimization algorithm (MOGOA), an improved MOGOA is proposed in this paper. The proposed algorithm is based on Sobol sequence, adaptive social force, cosine parameter c, as well as Levy flight mechanism (SACLMOGOA), where Sobol sequence is adopted to initialize the population, thereby improving the diversity and ergodicity of the initial population. The adaptive social force is proposed to enhance the global exploration ability in the early stage of iteration and the local development ability in the late stage of iteration. A cosine type parameter c is used to ensure sufficient exploration time and rapid convergence of the algorithm during position update process and Levy flight is used to guide some grasshoppers to mutate, enhancing the ability to escape from the local optima and improving the performance and efficiency of the algorithm. The proposed algorithm is tested with ZDT and DTLZ series benchmark test functions to validate its effectiveness, and it is also compared with conventional MOGOA, multi-objective particle swarm optimization, multi-objective gray Wolf, and multi-objective jellyfish algorithms. The simulation results demonstrate that the proposed algorithm outperforms other algorithms in terms of inverse generational distance (IGD), spread (SP), maximum spread (MS), run time and Wilcoxon rank sum test. Furthermore, the proposed algorithm is successfully applied to the capacity configuration of the urban rail hybrid energy storage systems (HESS) of Changsha Metro Line 1 in China, reducing the traction network voltage fluctuations by 3.3 % and 2.2 % compared to no HESS capacity configuration optimization, and by 14 % and 5.7 % compared to no HESS during train starting and breaking, respectively. While achieving the goal of energy-saving and voltage stabilization, the cost of the hybrid energy storage systems is minimized as well. All of these have demonstrated SACLMOGOA is an effective tool for solving complex multi-objective optimization problems in engineering.

A machine learning approach to optimize the performance of a combined solar chimney-photovoltaic thermal power plant

Solar Chimney Power Plant (SCPP) is a renewable energy system that indirectly converts solar energy to electricity. However, the efficiency of SCPP is not sufficient for practical applications. Integrating SCPPs with Photovoltaic-Thermal systems (PVT) could enhance their performance to levels acceptable for industrial adoption. This study investigates the combined SCPP-PVT performance for the weather conditions of Austin (Texas), San Diego (California), and Phoenix (Arizona), all on a similar latitude. Various configurations of this combined system are numerically simulated, and their efficiencies are compared with a conventional SCPP, SCPP-Photovoltaic (PV), and stand-alone PV modules. Moreover, to predict and optimize the performance of these systems, the Support Vector Regression with Linear (LSVR), Polynomial (PSVR), Gaussian (GSVR), and Hybrid (HSVR) kernels are implemented. In order to optimize the hyperparameters of the Machine Learning (ML) models, the Grey Wolf Optimizer (GWO) is implemented. Also, the optimum performance of the SCPP-PVT system is obtained using the Multi-objective Grasshopper Optimization Algorithm (MOGOA). The results show that the HSVR ML model has the highest accuracy, followed by PSVR, GSVR, and LSVR models. It is shown that the SCPP-PVT system outperforms both SCPP-PV and stand-alone PV modules, respectively. Finally, the SCPP-PVT is shown to outperform the PV modulus by up to 4.8%.

Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and Multi-objective Grasshopper Optimization Algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively.

A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data

Alzheimer’s disease (AD) is a neurodegenerative disease that affects a large number of people across the globe. Even though AD is one of the most commonly seen brain disorders, it is difficult to detect and it requires a categorical representation of features to differentiate similar patterns. Research into more complex problems, such as AD detection, frequently employs neural networks. Those approaches are regarded as well-understood and even sufficient by researchers and scientists without formal training in artificial intelligence. Thus, it is imperative to identify a method of detection that is fully automated and user-friendly to non-AI experts. The method should find efficient values for models’ design parameters promptly to simplify the neural network design process and subsequently democratize artificial intelligence. Further, multi-modal medical image fusion has richer modal features and a superior ability to represent information. A fusion image is formed by integrating relevant and complementary information from multiple input images to facilitate more accurate diagnosis and better treatment. This study presents a MultiAz-Net as a novel optimized ensemble-based deep neural network learning model that incorporate heterogeneous information from PET and MRI images to diagnose Alzheimer’s disease. Based on features extracted from the fused data, we propose an automated procedure for predicting the onset of AD at an early stage. Three steps are involved in the proposed architecture: image fusion, feature extraction, and classification. Additionally, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is presented as a multi-objective optimization algorithm to optimize the layers of the MultiAz-Net. The desired objective functions are imposed to achieve this, and the design parameters are searched for corresponding values. The proposed deep ensemble model has been tested to perform four Alzheimer’s disease categorization tasks, three binary categorizations, and one multi-class categorization task by utilizing the publicly available Alzheimer neuroimaging dataset. The proposed method achieved (92.3 ± 5.45)% accuracy for the multi-class-classification task, significantly better than other network models that have been reported.

Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

Recently, researchers in the power system industry have considered the intelligent parking lot idea. The intelligent parking lots (IPL) try to exchange power with the upstream by electric vehicles (EVs) charge/discharge application. The mentioned process of charge/discharge can provide a solution model of regular problems in this field, i.e., peak time problems. Additionally, such vehicles' existence can play an essential role in environmental performance enhancement in power systems. The primary purpose of this paper is to consider such enhancements of EVs in power systems and environmental performance. For this purpose, a multi-objective optimization approach is suggested for the environmental performance and economical operation of IPL by considering the time-of-use rates of a demand response program. Since such a model is associated with various practical bounds, the multi-objective grasshopper optimization algorithm is suggested to solve this problem. The outcomes indicate the effectiveness of the compared techniques utilizing fuzzy decision-making strategies. Chaos theory is employed to increase this method and progress searching operators. Additionally, the proposed multi-objective technique is a model created using fuzzy theory, variable detection, strategy selection-based memory, and non-dominated sorting theory to choose the best Pareto from a list of solutions with robust functionality for resolving the problems mentioned above. The proposed technique is tested on a sample system that includes an intelligent parking lot, local production facilities, and non-renewable and renewable production systems under the proposed uncertainty strategy of upstream grid pricing. The study showed the effectiveness of the suggested method. The simulation findings indicate that the emission rate and overall cost of intelligent parking lots are reduced by 3.87 and 2%. It indicates that economic and environmental objectives are also satisfied due to the successful deployment of a demand response program.

Using machine learning approaches to model and optimize a combined solar/natural gas-based power and freshwater cogeneration system

Problems of sustainable development and environmental protection pose a challenge to humanity unprecedented in scope and complexity. Whether and how the problems are resolved have significant implications for human and ecological well-being. Accordingly, this work proposes an electricity and freshwater cogeneration system using solar energy and natural gas dual sources. The proposed system is a combination of a heliostat field with a gas turbine cycle as the top systems and thermal vapor compression-multi effect desalination, steam Rankine cycle, organic Rankine cycle, and thermoelectric generator as subsystems. For performance analysis, energy, exergy, exergoeconomic, economic, and environmental analyses were performed. To optimize the system, a coupled model of the support vector regression, multi-objective grey wolf optimization algorithm, multi-objective Grasshopper optimization algorithm, and two different decision-making methods were suggested. According to obtained results, the best swirling number and pressure ratio were 0.95 and 7, respectively. Moreover, the exergy efficiency, total product cost rate, and CO2 emission were chosen as the best optimization scenario, which led to 45.6 % of exergy efficiency, 2.716 $/GJ of total product cost rate, and 30.26 kg/s of freshwater. Moreover, the total exergy destruction rate decreased from 15153 kW to 14820 kW after the optimization of the system.

Accurate combination forecasting of wave energy based on multiobjective optimization and fuzzy information granulation

Wave energy forecasting modeling is critical for promoting renewable energy storage technology as well as for energy sustainability and global carbon neutrality goals. However, due to the irregular volatility and complexity in wave energy data, all the effective information cannot be fully utilized by a traditional forecasting model; moreover, the point forecasting results cannot be used to effectively analyze the uncertainty of the time series. To overcome these shortcomings, a multistep point-interval combined significant wave height forecasting system based on the multiobjective grasshopper optimization algorithm and the fuzzy information granulation strategy is proposed to forecast the half-hour actual wave height at different buoy locations. Applying this system, Pareto optimal weights can be obtained to integrate the respective advantages of deep learning and neural network models in the combined forecasting module, achieve the best point and interval forecasting accuracy and accurately analyze the uncertainty of point forecasting results. Among the combined models, the proposed system has a more comprehensive and scientific prediction performance than other models (MAPE = 4.9866 for Site 1, MAPE = 4.9138 for Site 2, and MAPE = 3.9572 for Site 3). The forecasting outcomes indicate that the developed system significantly improves forecasting accuracy and stability, which provides reliable technical support for the sustainable development of wave power generation.

A Wind Speed Combination Forecasting Method Based on Multifaceted Feature Fusion and Transfer Learning for Centralized Control Center

• A novel combined model based on transfer learning is proposed for wind speed forecasting in different data features and scenarios. • MVMD is used to decompose the multivariate series with the intention of considering the internal coupling relationship between wind speed and meteorological series. • TDCN-BiLSTM model is established for alleviating the interference between data, extracting multi-scales characteristics. • The IMOGOA is developed to improve the optimization ability of the algorithm. With the establishment of remote centralized control centers for wind farms, the combined model of multiple deep neural networks used in most wind speed prediction methods can no longer meet the requirements of centralized control centers for efficient and low-cost wind speed prediction in different locations. Thus, a model based on multi-layer feature fusion and transfer learning is proposed in this paper for multi-location wind speed forecasting. Under the idea of "offline training and online prediction", the wind speed characteristics of wind farms at typical locations are learned in the offline stage by fusing multifaceted features through a two-channel dilated convolutional network and a bi-directional long short-term memory network, transferring the well-trained models to any target wind farm to achieve online prediction, and integrating the prediction results of each offline model with an improved multi-objective grasshopper optimization algorithm to further enhance the prediction accuracy. Finally, three comparisons reveal that the proposed model outperforms other baseline models in adaptability and accuracy.

Remote sensing scene classification using visual geometry group 19 model and multi objective grasshopper optimization algorithm

Remote sensing scene classification using visual geometry group 19 model and multi objective grasshopper optimization algorithm

Noisy Low-Illumination Image Enhancement Based on Parallel Duffing Oscillator and IMOGOA

In complex environment, the captured images face several kinds of problems, including low illumination and intensive noise, which deteriorates image quality and has a great impact on the follow-up work. In this work, inspired by stochastic resonance theory, we design a model that considers the spatial characteristics of image and noise reduction and enhancement are simultaneously realized. The 8-neighborhood pixel extraction method and the Duffing oscillator model are used to parallel process the image, and then the image details are restored by homomorphic filter. In order to optimize the parameters of parallel Duffing oscillator model and homomorphic filter adaptively, multiobjective grasshopper optimization algorithm is introduced into the method. Sobol sequence and differential mutation operators are used to improve the optimization algorithm, and the fitness function is constructed by using peak signal-to-noise ratio and standard deviation. To verify the effectiveness of the proposed method, low-illumination image data with Gaussian noise is used for subjective and objective evaluation. The experimental results show that the proposed algorithm gives prominence to useful information, which has smaller color distortion and better visual quality.

Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm

Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm