Multi-objective Grey Wolf Optimization Algorithm Research Articles

Research on Blast Furnace Ingredient Optimization Based on Improved Grey Wolf Optimization Algorithm

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO2 emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO2 emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met.

Research on Blast Furnace Ingredient Optimization Based on Improved Grey Wolf Optimization Algorithm

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO2 emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO2 emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met.

Machine learning-based optimization and 4E analysis of renewable-based polygeneration system by integration of GT-SRC-ORC-SOFC-PEME-MED-RO using multi-objective grey wolf optimization algorithm and neural networks

This study introduces a new solar-methane-driven setup that recovers waste heat from flue gases to produce electricity, hydrogen, oxygen, fresh water (FW), and domestic hot water (DHW) while reducing the environmental impact of gas turbines. This integration includes the Brayton cycle and solid oxide fuel cell as motive cycles, the steam and organic Rankine cycle, a parabolic trough solar collector (PTC), a proton exchange membrane electrolyzer, multi-effect distillation, and reverse osmosis. Using Engineering Equation Solver software, analyses in energy, exergy, exergoeconomic, and exergoenvironment (4E) are conducted to identify critical system evaluation parameters in thermodynamics, economics, and the environment. Additionally, the proposed system is optimized using an artificial neural network and the multi-objective grey wolf optimizer algorithm, utilizing decision variables identified through sensitivity analysis to enhance system performance. The results of six-objective optimization show energy and exergy efficiencies at 49.48 % and 47.21 %, with a net power production of 133 MW and a total cost rate of $7903/h. Among all the components, the combustion chamber and PTC have the maximum exergy destruction, with values of 58.87 MW and 17.29 MW, respectively. Also, hydrogen, FW, and DHW production rates are 201.6 kg/h, 43.88 kg/s, and 40.98 kg/s, respectively. Moreover, the costs of hydrogen, FW, and DHW are $953.64/h, $57.35/h, and $82.48/h, respectively. Finally, Bloemfontein, Las Vegas, and Tunis are the most favorable regions economically, with a low levelized cost of electricity of $0.05931, $0.06006, and $0.05932 per kWh, along with payback periods of 0.6144, 0.5524, and 1.069 years, respectively.

Analysis and optimization of a sustainable hybrid solar-biomass polygeneration system for production of power, heating, drying, oxygen and ammonia

In this study, we introduce an innovative polygeneration system that combines solar and biomass energies to generate power, heating, hot air for the drying process, oxygen, and ammonia. The proposed polygeneration system comprises a solar tower subsystem (STS) integrated with two energy storage tanks (cold and hot), a gas turbine cycle (GTC), an organic Rankine cycle (ORC), a proton exchange membrane (PEM) electrolyzer, a thermoelectric generator (TEG), a cryogenic air separation unit (CASU), and an ammonia synthesis reactor (ASR). The GTC and the ORC equipped with the TEG are utilized for power production. The steam for the gasification process, hot air for the drying process and heating are produced by three regenerators. The PEM electrolyzer is used for the decomposition of water into hydrogen and oxygen, while the air separator (cryogenic method) is employed for the separation of air into nitrogen and oxygen. The separated oxygen is stored in an oxygen tank, while nitrogen and hydrogen are utilized for the ammonia generation. The system is evaluated from energy, exergy, economic and, environmental standpoints. Based on the findings, the proposed system is capable of generating approximately 163 MW of net power, 150 MW of heating, 100 MW of drying, 21.7 kmol/hr of oxygen and 23.24 kg/hr of ammonia. The total energy and exergy efficiencies, as well as the life cycle cost (LCC) and CO2 emissions are determined to be 70.68 %, 48.71 %, 1.175 × 109 $ and 381.3 kg/MWh, respectively. Additionally, a multi-objective Grey Wolf optimization algorithm is utilized to optimize the exergy efficiency, LCC, and CO2 emissions. The optimized results yield an exergy efficiency of 51.02 %, an LCC of 1.16 × 109 $, and a CO2 emissions of 359.73 kg/MWh. The proposed system demonstrates encouraging thermodynamic and economic performances, providing an appealing solution for efficient utilization of abundant solar and biomass resources.

Thermodynamic and exergoeconomic assessment of a trigeneration system driven by a biomass energy source for power, cooling, and heating generation

Environmental concerns and the limitations of conventional energy drive the exploration of reliable renewable sources. Herein, a biomass-assisted trigeneration system in conjunction with a methane-fed gas turbine cycle for generating power, cooling, and heating, and an attempt has been made to maximize the energy extraction from the energy source. The proposed system includes a steam Rankine cycle, a gas turbine unit, and a Kalina subsystem, and is investigated from energetic, exergetic, and economic viewpoints. A comprehensive parametric evaluation is carried out, and a multi-objective grey wolf optimization algorithm integrated with the LINMAP decision-maker method is employed to detect the most optimum point through two different scenarios. By conducting a base study, a net output power of 5926 kW, cooling load of 36.56 kW, heating load of 843.4 kW, and an energetic efficiency of 47.41 % can be achieved. The exergetic and economic standpoints bring out an exergy destruction rate of 7697.07 kW, cycle exergetic efficiency of 41.44 %, and a net present value of 12.420 $M that sequels to a payback period of 5.56 years. Biomass proves reliable in multigeneration systems, emphasizing the pivotal role of studies in maximizing energy extraction for diverse product outputs.

Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and group method of data handling artificial neural networks

In this article, the ability of GMDH artificial neural networks (ANNs) to predict the rheological behavior (RB) of nanofluids (NFs) containing CuO NPs is studied. ANNs are a powerful mathematical tool that can identify the relationship among the parameters without the need to extract the relationship among them. The main purpose of this study is to use the GMDH ANN method to generate and predict the viscosity (μ) parameter using several input variables (IPV) such as solid volume fraction (SVF), nanoparticles (NPs), temperature (Temp), and shear rate (SR). By pairing the GMDH ANN with the evolutionary algorithm, this capability is created so that the values predicted by the ANN are more compatible with the laboratory numbers. The evolutionary algorithms (EAs) used in this study include three algorithms: Non-Dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf Optimizer (MOGWO). These algorithms are selected for optimization, among which the best performance is related to the coupling of GMDH ANN with the MOGWO algorithm. In the next step, the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Grey Wolf Optimizer (GWO) algorithms are used. This process is done to minimize the target function (TF) (μ) and evaluate the optimal points. According to the obtained results, among the EAs used in this study, the best performance belongs to the GA algorithm. Finally, in the last part of this study, the most optimal mode for IPV and output variable (OPV) of TF is determined. Numerically, the values of IPV data, such as SVF, T, and SR, are respectively 0.2242%, 50, and 246.7427, and the most optimal value for the OPV of TF (μ) was estimated as 0.96686 cP.

Optimal allocation of multiple capacitors in a hybrid AC/DC microgrid for power quality improvement

Along with the various features for implementing the Hybrid AC/DC Microgrid (HMG), this article proposes an approach for optimal allocation of multiple capacitors which are investigated in a proposed modeling based on the IEEE 14-bus distribution system. The power quality of the HMG has been investigated during the urgent intermittent of Distributed Energy Resources (DERs) and Reactive Power Compensation (RPC) methods. Moreover, the investigation has been achieved in the presence of unbalanced loads and nonlinear loads with maximum and minimum demand scenarios. To cope with the power quality concerns in the studied cases, the fixed capacitor bank as an RPC system in Medium Voltage (MV) level load buses has been utilized. Although the performance indices of the power quality improved in MV-level buses, the Low Voltage (LV) level load buses still endure extensive operation performance deteriorations caused by unbalanced loads. Therefore, in this article, a compensation scheme applied in LV-level load buses and MV-level buses has been proposed consistent with the power flow computations. The Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm is implemented to optimize both the size and location of capacitor banks over different voltage levels with high accuracy. The comprehensive assessment and discussion of the simulation results demonstrate the superiority of utilizing the proposed compensation scheme in both MV-level and LV-level load buses. Hence, the power quality is not only enhanced but also the installation cost is reduced. The complete model of the studied system has been validated using MATLAB/ Simulink.Article HighlightsThe reactive power compensation method is used to improve the power quality in a hybrid AC-DC microgrid.The applied RPC method verifies using the MOGWO algorithm to optimize both the location and size of multiple capacitor banks.Power quality challenges and future research trends are debated.

Thermodynamic analysis and operation strategy optimization of coupled molten salt energy storage system for coal-fired power plant

The growing use of renewable energy requires greater flexibility than existing thermal power units. A steam-extraction system was developed to adjust the power output during a low-load period. The incorporation of molten-salt energy storage enables the decoupling of the boiler from the turbine, thus enabling the regulation of the output power during low-load operation. And the impact of key parameters on the performance of coal-fired units is analyzed to find the suitable operation parameters for the existing coal-fired power plant. The performance parameters of the system are optimized by a multi-objective grey wolf optimization algorithm, which conducts trade-off optimization between roundtrip efficiency and exergy efficiency to identify the Pareto Front for a certain range of extracted steam mass flow rates, hot-tank temperatures, and split ratios. The typical operating conditions of the combined system were analyzed, and a peak capacity of 64.1 MW and a power change factor of 17.33 % were achieved. The discharging process reduced the heat consumption of the unit, while the peak capacity and heat consumption of the combined system were used as objective functions to optimize the charging process. The peak capacity, power enhancement rate, and net profits of the system improved 1.12, 1.42, and 1.27 times after optimization. Heat consumption and efficiency were reduced by 2.61 % and 3.96 %. The molten-salt thermal storage system achieved a synergistic improvement in the operational flexibility and thermal efficiency of the thermal power system.

Interval multi-objective grey wolf optimization algorithm based on fuzzy system

PurposeThe interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.Design/methodology/approachFirst, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.FindingsThe experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.Originality/valueThis study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.

Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory

To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty.

A Multistep Interval Prediction Method Combining Environmental Variables and Attention Mechanism for Egg Production Rate

The egg production rate is a crucial metric in animal breeding, subject to biological and environmental influences and exhibits characteristics of small sample sizes and non-linearity. Currently, egg production rate prediction research predominantly focuses on single-step point prediction, lacking multistep and interval prediction exploration. To bridge these gaps, this study proposes a recursive, multistep interval prediction method for egg production rates, integrating environmental variables and attention mechanisms. Initially, this study employed three gradient boosting tree models (XGBoost, LightGBM, CatBoost) and the recursive feature elimination (RFE) method to select critical environmental variables and reduce data dimensionality. Subsequently, by scaling the time scale of important environmental variables and utilizing the variational modal decomposition improved by the grey wolf optimization (GWO-VMD) method for time-series decomposition, the volume of important environmental variable data is augmented and its complexity is reduced. Applying the long short-term memory (LSTM) neural network to obtain direct multistep predictions on IMFs, the predicted outcomes are averaged daily to yield the environmental variables for the upcoming two days. Finally, a multistep interval prediction model based on Seq2seq-Attention and Gaussian distribution is proposed in this study, and parameter optimization is carried out using the multi-objective grey wolf optimization algorithm (MOGWO). By inputting the historical egg production rate data and environmental variables into the proposed model, it is possible to achieve multistep point and interval prediction of egg production rates. This method was applied to analyze a dataset of egg production rates of waterfowl. The study demonstrated the feasibility of the recursive multistep prediction approach combined with environmental variables and guides egg production estimation and environmental regulation in animal husbandry.

Modeling and multi-objective optimization of time, greenhouse gas emissions, and resources for sustainable construction projects

Traditionally, project scheduling and environmental impact assessment are carried out separately which is ill-suited for modern organizations as creating a project plan that optimizes for environmental goals can have an adverse effect on project time and resource utilization efficiency. This study aims to address this issue by considering project time, greenhouse gas emissions, and resource-leveling objectives simultaneously. Multi-objective Grey Wolf Optimization algorithm has been modified by incorporating genetic operators and a local search heuristic and its performance was compared with the elitist non-dominated sorting genetic algorithm II on six assessment metrics. The computational experiments exhibited that the modified multi-objective Grey Wolf Optimization algorithm outdid the non-dominated sorting genetic algorithm on five out of six assessment metrics. Two case studies have been used to demonstrate the applicability and validity of the presented model. In the obtained Pareto front solutions for the real-life case study, the range of time, greenhouse gas emissions, and resource deviation have been 594–835 (days), 19,870,953.1-20,938,469.6 (kgCO2-e) and 67,204.93-141,915.55 (unitless) respectively. The examination of tradeoffs between objectives showed that greenhouse gas emissions can be decreased if the makespan and resource deviation are allowed a slight increase at various decision points. The presented model can help practitioners and researchers make practical tradeoffs between the conflicting goals of duration, greenhouse gas emissions, and resource optimization.

The feasibility of integrating three different thermal desalination units and a geothermal cycle combined with a modified Kalina cycle

To upsurge the geothermal energy-based freshwater production capacity, a novel waste heat recovery model for a geothermal cycle combined with a modified Kalina cycle is designed, investigated, and optimized in this study. The designed model relies on temperature control and efficient heat recovery capable of combining three different thermal desalination units, including multi-effect, humidification dehumidification, and separation vessel units. To ensure the possibility of integration, a coherent multi-aspect sensitivity study is conducted from thermodynamic and exergoeconomic points of view. In addition, two principal optimization scenarios are regarded and programmed in which total exergy destruction rate/output freshwater and net present value/fixed capital investment cases are included. To do optimization, a multi-objective grey wolf optimization algorithm is developed and utilized. According to the results of the sensitivity study, it is elicited that the variability of studied thermodynamic and cost-based metrics is more exposed to the variation in the inlet temperature of the separation vessel desalination. Besides, the first optimization scenario exhibits that the exergy destruction and freshwater production rates are gauged at 640.9kW and 0.619kg/s, correspondingly. Furthermore, in the second scenario, the net present value and fixed capital investment are obtained to be 164,638 $ and 225,653 $, respectively.

An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle

Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively.

Designing an Electric Transit Route Network Utilizing Energy Storage Technology to Mitigate Annual Demand Charge

The electrification of traditional transit route networks has been a promising option for urban public transit. Compared with fast chargers, energy storage (ES) technology benefits the planner in relation to cost savings and avoiding high electricity demand charges. Although many studies ignored the potentiality of utilizing ES chargers, this study uses this concept with demand charges to design an electric transit route network by proposing a bi-level optimization model. The upper-level model aims to minimize the total travel times of passengers along with the total number of indirect trips. In comparison, the lower-level problem plans to minimize the total costs of purchasing two types of fast and ES chargers and demand charges for the transit network that entirely operates with battery electric buses (BEBs). Because of the NP-hard nature of the proposed model, the discrete multi-objective grey wolf optimizer algorithm is suggested in a discrete space to search for a feasible solution by providing the Pareto frontier of two objective functions. Also, the initial route set generation algorithm has been applied to generate a new set of transit routes. To validate the proposed model, our model has been tested on a benchmark network of Mumford0 in a small and a Fargo–Moorhead area in a medium size. Our results confirmed that considering ES chargers could save 4.8% and 76.5% (for the user cost), 2.3%, and 111.9% (for the operator cost) of the total network and demand charges.

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.

An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system

This paper presents a novel robust fuzzy adaptive integral sliding mode controller designed via a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system. To this end, proper integral surfaces are defined and a sliding mode stabilizer is designed to converge the system errors to zero. Next, the gradient descent approach is employed to tune the design gains of the integral sliding mode controller. Then, fuzzy rules are employed to regulate the coefficients of the proposed robust control approach. In order to acquire the proper parameters of the proposed controller and avoid trial-and-error processes, a multi-objective grey wolf optimization algorithm is employed to enhance the performance of the proposed controller. The challenging case study of a Duffing-Holmes oscillator, as a nonlinear autonomous uncertain chaotic system without stable equilibrium points, is considered to assess the behavior of the suggests optimal robust fuzzy adaptive integral sliding mode controller. The results of this study are compared with the outcomes of a distinguished work in the literature. Lastly, the discussions elucidate the efficiency of the proposed controller with respect to uncertain chaotic nonlinear systems in terms of optimal control inputs and minimum tracking error.

Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing

The revolution of IoT and its capabilities to serve various fields led to generating a large amount of data for processing. Tasks that require an instant response, especially with sensitive delay tasks send to the fog node due to the close distance, and the complex tasks transfer to the cloud data center for its huge computation and storage. However, sending tasks to the fog decreases the transmission delay. Still, it increases the energy consumption of the end users, while transferring tasks to the cloud reduces users’ energy consumption but increases the transmission delay due to the long distance; besides, assigning tasks to appropriate resources compatible with task requirements. These are the main challenges in cloud-fog computing that need to improve. Thus, this study proposed a Multi-Objectives Grey Wolf Optimizer (MGWO) algorithm to reduce the QoS objectives delay and energy consumption and held in the fog broker, which plays an essential role in distributing tasks. The simulation result verifies the effectiveness of the MGWO algorithm compared to the state-of-the-art algorithms in reducing delay and Energy consumption.

Energy Trilemma Index-Based Multiobjective Optimal Sizing of PV-Battery System for a Building in Tropical Savanna Climate

Traditional buildings are responsible for carbonizing the environment by consuming a substantial amount of overall energy generation and are held accountable for violating the indexes of energy trilemma (ET). Optimally sizing the renewable energy sources (RES) with a battery system enhances the energy reliability and affordability that can address the challenges of ET indexes (ETI). To that end, an ETI-based multiobjective optimal sizing (MOOS) of PV-battery systems for a building situated in a tropical savanna climate (TSC) has been proposed. Objective functions based on ETI viz., loss of power supply probability (LPSP) that ensures energy security/reliability and levelized cost of energy (LCOE) that indicates energy affordability are formulated. An additional index named surplus energy generation (SEG) that minimizes the excess energy generated (EEG) from the PV which could not be sold back to the utility grid is also formulated. A multiobjective grey wolf optimization algorithm (MOGWO) for optimal sizing of PV-battery systems is utilized to resolve the challenges of the ETI. The Euclidean distance-based sorting approach is implemented to find the best optimal solution from the Pareto front of the MOGWO. Further, a techno-economic and reliability analysis of the optimized PV battery system is presented.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.