Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Heuristic-Based Optimal Load Frequency Control with Offsite Backup Controllers in Interconnected Microgrids

The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.

Optimal design of the fillet weld fastening the wind turbine column

This paper deals with the optimal design of the fillet weld of wind turbine column subjected to bending moment.Under the premise of determined the force acting on the column,in order to further optimize the fillet weld,the minimum volume of corner seam was determined in the case of non-linear design constraints.The constraints relate to the maximal stresses and fatigue of welding seam.A numerical solution to this problem is given by genetic optimization algorithm.The optimisation calculation result indicated that the active condition(constraint)was the stress from the static load.Useful and meaningful information is provided for the engineering field.

Simulation and optimization of scrap wagon dismantling system based on Plant Simulation

Based on the existing plant layout and process flow,a simulation analysis was conducted using the Plant Simulation platform with the utilization efficiency of each station and production capacity of the dismantling system as indicators.A problem with long-term suspension in the disassembly process was determined.Based on the two optimization directions of increasing material transportation equipment and expanding the buffer capacity,a cost-oriented optimization model is established.A genetic algorithm and model simulation were used to solve the model.An optimization scheme that satisfies the production needs and has the lowest cost is proposed.The results show that the optimized dismantling system solves the suspended work problem at the dismantling station and a significant improvement in productivity and station utilization efficiency compared with the previous system.

Topology Optimization of Stiffener Layout Design for Box Type Load-Bearing Component under Thermo-Mechanical Coupling

The structure optimization design under thermo-mechanical coupling is a difficult problem in the topology optimization field.An adaptive growth algorithm has become a more effective approach for structural topology optimization.This paper proposed a topology optimization method by an adaptive growth algorithm for the stiffener layout design of box type load-bearing components under thermo-mechanical coupling.Based on the stiffness diffusion theory,both the load stiffness matrix and the heat conduction stiffness matrix of the stiffener are spread at the same time to make sure the stiffener grows freely and obtain an optimal stiffener layout design.Meanwhile,the objectives of optimization are the minimization of strain energy and thermal compliance of the whole structure,and thermo-mechanical coupling is considered.Numerical studies for square shells clearly show the effectiveness of the proposed method for stiffener layout optimization under thermo-mechanical coupling.Finally,the method is applied to optimize the stiffener layout of box type load-bearing component of themachining center.The optimization results show that both the structural deformation and temperature of the load-bearing component with the growth stiffener layout,which are optimized by the adaptive growth algorithm,are less than the stiffener layout of shape‘#’stiffener layout.It provides a new solution approach for stiffener layout optimization design of box type load-bearing components under thermo-mechanical coupling.

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.

The Development of Highly Loaded Turbine Rotating Blades by Using 3D Optimization Design Method of Turbomachinery Blades Based on Artificial Neural Network & Genetic Algorithm

In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.

Optimization of Load Assignment to Boilers in Industrial Boiler Plants

Along with the increasing importance of sustainable energy, the optimization of load assignment to boilers in an industrial boiler plant becomes one of the major projects for the optimal operation of boiler plants. Optimal load assignment for power systems has been a long-lasting subject, while it is quite new for industrial boiler plants. The existing methods of optimal load assignment for boiler plants are explained and analyzed briefly in the paper. They all need the fuel cost curves of boilers. Thanks to some special features of the curves for industrial boilers, a new model referred to as minimized departure model (MDM) of optimization of load assignment for boiler plants is developed and proposed in the paper. It merely relies upon the accessible data of two typical working conditions to build the model, viz. the working conditions with the highest efficiency of a boiler and with no-load. Explanation of the algorithm of computer program is given, and effort is made so as to determine in advance how many and which boilers are going to work. Comparison between the results using MDM and the results reported in references is carried out, which proves that MDM is preferable and practicable.

A Hybrid Optimization Technique Coupling an Evolutionary and a Local Search Algorithm for Economic Emission Load Dispatch Problem

This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. To improve the solution quality, local search technique was applied as neighborhood search engine, where it intends to explore the less-crowded area in the current archive to possibly obtain more non-dominated solutions. TOPSIS technique can incorporate relative weights of criterion importance, which has been implemented to identify best compromise solution, which will satisfy the different goals to some extent. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.

The Optimal Steam Pressure of Thermal Power Plant in a Given Load

As the large change of the grid load, many large capacity units of our country had to change the load in order to meet the gird need. When a thermal power plant receives a given load instruction from the grid, it is necessary to set an optimal steam pressure to maintain the high efficiency of the plant. In the past optimization methods, during the process of calculation, the output of the turbine often changed, it was hard to maintain the output constant. Therefore, in combination with the theory of variable condition of turbine, calculation of governing stage and the matrix equation of thermal power system, an optimization method were put forward and an optimal solution was got in a given load.

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

Multiobjective optimization scheme for industrial synthesis gas sweetening plant in GTL process

In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using Non-Dominated Sorting Genetic Algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of （DEA/MDEA） mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ？ C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ？ C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery.

Multi-objectives nonlinear structure optimization for actuator in trajectory correction fuze subject to high impact loadings

This paper presents an actuator used for the trajectory correction fuze,which is subject to high impact loadings during launch.A simulation method is carried out to obtain the peak-peak stress value of each component,from which the ball bearings are possible failures according to the results.Subsequently,three schemes against impact loadings,full-element deep groove ball bearing and integrated raceway,needle roller thrust bearing assembly,and gaskets are utilized for redesigning the actuator to effectively reduce the bearings’stress.However,multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield stress.Four gasket’s structure parameters and three bearings’peak-peak stress are served as the four optimization variables and three objectives,respectively.Optimized Latin hypercube design is used for generating sample points,and Kriging model selected according to estimation result can establish the relationship between the variables and objectives,representing the simulation which is time-consuming.Accordingly,two optimization algorithms work out the Pareto solutions,from which the best solutions are selected,and verified by the simulation to determine the gaskets optimized structure parameters.It can be concluded that the simulation and optimization method based on these components is effective and efficient.

Multi-objective optimization of high-sulfur natural gas purif ication plant

There exists large space to save energy of high-sulfur natural gas purification process.The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas.A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax.Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution.To solve the problem that the process model does not converge in some conditions,back-propagation(BP)neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model.The uniform design method and the table U21(107)have been applied to design the experiment points for training and testing BP model.High prediction accuracy can be achieved by using the BP model.Nondominated sorting genetic algorithm-II has been developed to optimize the two objectives,and 100 Pareto optimal solutions have been obtained.Three optimal points have been selected and evaluated further.The results demonstrate that the total comprehensive energy consumption is reduced by 13.4%and the production rate of purified gas is improved by 0.2%under the optimized operating conditions.

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.

An optimization-oriented modeling approach using input convex neural networks and its application on optimal chiller loading

Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.

Robust Optimization Method of Cylindrical Roller Bearing by Maximizing Dynamic Capacity Using Evolutionary Algorithms

Optimization of cylindrical roller bearings(CRBs)has been performed using a robust design.It ensures that the changes in the objective function,even in the case of variations in design variables during manufacturing,have a minimum possible value and do not exceed the upper limit of a desired range of percentage variation.Also,it checks the feasibility of design outcome in presence of manufacturing tolerances in design variables.For any rolling element bearing,a long life indicates a satisfactory performance.In the present study,the dynamic load carrying capacity C,which relates to fatigue life,has been optimized using the robust design.In roller bearings,boundary dimensions(i.e.,bearing outer diameter,bore diameter and width)are standard.Hence,the performance is mainly affected by the internal dimensions and not the bearing boundary dimensions mentioned formerly.In spite of this,besides internal dimensions and their tolerances,the tolerances in boundary dimensions have also been taken into consideration for the robust optimization.The problem has been solved with the elitist non-dominating sorting genetic algorithm(NSGA-II).Finally,for the visualization and to ensure manufacturability of CRB using obtained values,radial dimensions drawing of one of the optimized CRB has been made.To check the robustness of obtained design after optimization,a sensitivity analysis has also been carried out to find out how much the variation in the objective function will be in case of variation in optimized value of design variables.Optimized bearings have been found to have improved life as compared with standard ones.

Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

Optimal Tuning of Plant-Friendly PID Controllers

A plant-friendly proportional-integral-derivative （PID） controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and robustness.The objective function is chosen as the weighted sum of the integral of squared time-weighted error and the integral of squared timeweighted derivative of the control variable with respect to set-point response,while the robustness of the system is guaranteed by constraints on gain and phase margins.Due to the complex structure of the constraints,the problem is solved by genetic algorithms.Simulation analysis show the proposed method could efficiently reduce the controller output variations while maintaining a short settling time.Based on the simulation results,iterative tuning rules for the weighting factor in the objective function are obtained,which allows efficient simple proportional-integral（PI） tuning formulae to be derived.

Optimal Design of Compliant Trailing Edge for Shape Changing

Adaptive wings have long used smooth morphing technique of compliant leading and trailing edge to improve their aerodynamic characteristics. This paper introduces a systematic approach to design compliant structures to carry out required shape changes under distributed pressure loads. In order to minimize the deviation of the deformed shape from the target shape, this method uses MATLAB and ANSYS to optimize the distributed compliant mechanisms by way of the ground approach and genetic algorithm （GA） to remove the elements possessive of very low stresses. In the optimization process, many factors should be considered such as airloads, input displacements, and geometric nonlinearities. Direct search method is used to locally optimize the dimension and input displacement after the GA optimization. The resultant structure could make its shape change from 0 to 9.3 degrees. The experimental data of the model confirms the feasibility of this approach.