Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Variations of Snow Temperature and their Influence on Snow Cover Physical Parameters in the Western Tianshan Mountains,China

This article discussed about snow temperature variations and their impact on snow cover parameters. Automatic temperature recorders were used to sample at lo-minute intervals at the Tianshan Station for Snow-cover and Avalanche Research, Chinese snow temperature Academy of Sciences. lo-layer and the snow cover parameters were measured by the snow property analyzer （Snow Fork） in its Stable period, Interim period and Snow melting period. Results indicate that the amplitude of the diurnal fluctuation in the temperature during Snow melting period is 1.62 times greater than that during Stable period. Time up to the peak temperature at the snow surface lags behind the peak solar radiation by more than 2.5 hours, and lags behind the peak atmospheric temperature by more than 0.2 hours during all three periods. The optimal fitted function of snow temperature profile becomes more complicated from Stable period to Snow melting period. 22 h temperature profiles in Stable period are the optimal fitted by cubic polynomial equation. In Interim period and Snow melting period, temperature profiles are optimal fitted by exponential equation between sunset and sunrise, and by Fourier function when solar radiation is strong. The vertical gradient in the snow temperature reaches its maximum value at the snow surface for three periods. The peak of this maximum value occurs during Stableperiod, and is 4.46 times greater than during Interim period. The absolute value of temperature gradient is lower than 0.1℃ cm-1 for 30 cm beneath snow surface. Snow temperature and temperature gradient in Stable period-Interim period indirectly cause increase （decrease） of snow density mainly by increasing （decreasing） permittivity. While it dramatically increases its water content to change its permittivity and snow density in Snow melting period.

Joint Optimization of Channel Allocation,Link Assignment and Power Control for Device-to-Device Communication Underlaying Cellular Network

Device-to-Device(D2D) communication has been proposed to facilitate cellular network with system capacity(SC) and quality of service(QoS).We consider the design of link assignment(LA),channel allocation(CA)and power control(PC) in D2D-aided content delivery scenario for both user fairness(UF)and system throughput(ST) under QoS requirement.Due to the complexity of the problem,we decompose it into two components:CA is formulated from graph perspective to mitigate severe co-channel interference,which turns out to be the Max K-cut problem;LA and PC are jointly optimized to utilize the gain achieved from CA for supreme performance,and specifically,genetic algorithm(GA) is adopted to optimize LA,but when deriving the fitness of each chromosome,PC optimization will be involved.Thanks to numerical results,we elucidate the efficacy of our scheme.

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.

Optimization of dispersed carbon nanoparticles synthesis for rapid desulfurization of liquid fuel

Stringent regulations and environmental concerns make the production of clean fuels with low sulfur content compulsory for the petroleum refining industry.Because of ease of operation without high energy consumption,the adsorption of sulfur compounds seems the most promising process.Central composite design was used to optimize parameters influencing the synthesis of dispersed carbon nanoparticles（CNPs）,a new class of sorbents,in order to obtain an excellent adsorbent for desulfurization of liquid fuel.The optimized dispersed CNPs,which are immiscible in liquid fuel,can effectively adsorb different benzothiophenic compounds.Equilibrium adsorption was achieved within 2 min for benzothiophene,dibenzothiophene,and 4,6-dimethyldibenzothiophene with removal efficiency values of 75 %,83 %,and 52 %,respectively.The rate of desulfurization by the prepared CNPs in the present work is seven times higher than the previously reported CNPs.Optimized CNPs were characterized by different techniques.Finally,the effect of the mass of CNPs on the removal efficiency was studied as well.

Study on the performance of lightweight roadway wall thermal insulation coating containing EP-GHB mixed ceramsite

As the mining depth increases,the problem of high-temperature thermal damage mainly caused by heat dissipation of surrounding rock is becoming more and more obvious.It is very important to solve the environmental problem of mine heat damage to improve the efficiency of mineral resource exploitation and protect the physical and mental health of workers.One can apply thermal insulation coating on the walls of mine roadways as a means of implementing active heat insulation.In this paper,expanded perlite(EP)and glazed hollow bead(GHB)are used as the main thermal insulation materials,ceramsite and sand as aggregate,plus glass fiber and sodium dodecyl sulfate to develop a new lightweight composite thermal insulation coating through orthogonal experiment method.According to the plate heat flow meter method and mechanical test method,the thermal insulation and mechanical properties of EP-GHB mixed ceramsite coating were studied by making specimens with different parameter ratios,and according to the analysis of the experimental results,the optimal mix ratio of the coating was selected.In addition,Fluent numerical simulation software was used to establish the roadway model,and the thermal insulation effect of the coating in the roadway under different working conditions was studied.The results show that the thermal conductivity of the prepared composite thermal insulation coating material is only 8.5% of that of ordinary cement mortar,and the optimal thickness of adding thermal insulation coating is 0.2 m,which can reduce the outlet air temperature of the roadway with a length of 1000 m by 4.87 K at this thickness.The thermal insulation coating developed in this study has the advantages of simple technology and strong practicability,and has certain popularization and application value in mine heat damage control.

Artificial neural network with modified rider optimization for design and control of PV-integrated quasi Z-source cascaded multilevel inverter system

The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models.However,such processes are limited to known system models,which are uncertain in future systems.Here,the controller for 3-phase qZS-CMI is modeled based on two phases,and the source PV voltage and output grid current are controlled.In Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid current.For two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is used.Moreover,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid current.The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm.The main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid current.Hence,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.