Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.

Topology Optimization of Metamaterial Microstructures for Negative Poisson’s Ratio under Large Deformation Using a Gradient-Free Method

Negative Poisson’s ratio(NPR)metamaterials are attractive for their unique mechanical behaviors and potential applications in deformation control and energy absorption.However,when subjected to significant stretching,NPR metamaterials designed under small strain assumption may experience a rapid degradation in NPR performance.To address this issue,this study aims to design metamaterials maintaining a targeted NPR under large deformation by taking advantage of the geometry nonlinearity mechanism.A representative periodic unit cell is modeled considering geometry nonlinearity,and its topology is designed using a gradient-free method.The unit cell microstructural topologies are described with the material-field series-expansion(MFSE)method.The MFSE method assumes spatial correlation of the material distribution,which greatly reduces the number of required design variables.To conveniently design metamaterials with desired NPR under large deformation,we propose a two-stage gradient-free metamaterial topology optimization method,which fully takes advantage of the dimension reduction benefits of the MFSE method and the Kriging surrogate model technique.Initially,we use homogenization to find a preliminary NPR design under a small deformation assumption.In the second stage,we begin with this preliminary design and minimize deviations in NPR from a targeted value under large deformation.Using this strategy and solution technique,we successfully obtain a group of NPR metamaterials that can sustain different desired NPRs in the range of[−0.8,−0.1]under uniaxial stretching up to 20% strain.Furthermore,typical microstructure designs are fabricated and tested through experiments.The experimental results show good consistency with our numerical results,demonstrating the effectiveness of the present gradientfree NPR metamaterial design strategy.

Optimal scheduling of a township integrated-energy system using the adjustable heat-electricity ratio model

With the expansion and implementation of rural revitalization strategies,there is a constant need for new energy sources for the construction of new townships.Consequently,integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly.Biomass energy,a renewable green energy source with low pollution and wide distribution,has significant application potential in integrated energy systems.Considering the application of biomass energy in townships,this study established an integrated biomass energy system and proposed a model to optimize its operation.Lowest economic cost and highest clean energy utilization rate were considered as the objective functions.In addition,a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass.Finally,a simulation analysis conducted for a town in China was discussed,demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate.Moreover,by adjusting the heat-electricity ratio,the economic cost was further reduced by 6.70%,whereas the clean energy utilization rate was increased by 5.14%.

Optimization of Open-cast Mining Procedure Based on RSR Method

To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.

A Modified Bi-Directional Evolutionary Structural Optimization Procedure with Variable Evolutionary Volume Ratio Applied to Multi-Objective Topology Optimization Problem

Natural frequency and dynamic stiffness under transient loading are two key performances for structural design related to automotive,aviation and construction industries.This article aims to tackle the multi-objective topological optimization problem considering dynamic stiffness and natural frequency using modified version of bi-directional evolutionary structural optimization(BESO).The conventional BESO is provided with constant evolutionary volume ratio(EVR),whereas low EVR greatly retards the optimization process and high EVR improperly removes the efficient elements.To address the issue,the modified BESO with variable EVR is introduced.To compromise the natural frequency and the dynamic stiffness,a weighting scheme of sensitivity numbers is employed to form the Pareto solution space.Several numerical examples demonstrate that the optimal solutions obtained from the modified BESO method have good agreement with those from the classic BESO method.Most importantly,the dynamic removal strategy with the variable EVR sharply springs up the optimization process.Therefore,it is concluded that the modified BESO method with variable EVR can solve structural design problems using multi-objective optimization.

Optimization of clay material mixture ratio and filling process in gypsum mine goaf

Because there is neither waste rock nor mill tailings in the gypsum mine, and the buildings on the goaf of gypsum mine are needed to be protected, the research proposed the scheme of the clay filling technology. Gypsum, cement, lime and water glass were used as adhesive, and the strength of different material ratios were investigated in this study. The influence factors of clay strength were obtained in the order of cement, gypsum, water glass and lime. The results show that the cement content is the determinant influence factor, and gypsum has positive effects, while the water glass can enhance both clay strength and the fluidity of the filing slurry. Furthermore, combining chaotic optimization method with neural network, the optimal ratio of composite cementing agent was obtained. The results show that the optimal ratio of water glass, cement, lime and clay (in quality) is 1.17:6.74:4.17:87.92 in the process of bottom self-flow filling, while the optimal ratio is 1.78:9.58:4.71:83.93 for roof-contacted filling. A novel filling process to fill in gypsum mine goaf with clay is established. The engineering practice shows that the filling cost is low, thus, notable economic benefit is achieved.

Optimization of the reflux ratio of benzene-toluene stage distillation columns by the Cuckoo algorithm

In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The Cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary aim of MANETs is to extendflexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing system.An Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In order to provide an energy-efficient solution and to minimize network traffic,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in nodes.According to the researchfindings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

Multidisciplinary Design Optimization of Crash Box with Negative Poisson’s Ratio Structure

To improve the crashworthiness and energy absorption performance,a novel crash box negative Poisson’s ratio(NPR)structure is proposed according to the characteristics of low speed collision of bumper system.Taking the peak collision force and the average collision force as two subsystems,a multidisciplinary collaborative optimization design is carried out,and its optimization results are compared with the ones optimized by NSGA-II algorithm.Simulation results show that the crashworthiness and energy absorption performance of the novel crash box is improved effectively based on the multidisciplinary optimization method.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

Aeroelastic Analysis and Optimization of High-aspect-ratio Composite Forward-swept Wings

In order to analyze the effects of forward-swept angle and skin ply-orientation on the static and dynamic aeroelastic characteristics, the aeroelastic modeling and calculation for high-aspect-ratio composite wings with different forward-swept angles and skin ply-orientation are performed. This paper presents the results of a design study aiming to optimize wings with typical forward-swept angles and skin ply-orientation in an aeroelastic way by using the genetic/sensitivity-based hybrid algorithm. Under the conditions of satiated multiple constraints including strength, displacements, divergence speeds and flutter speeds, the studies are carried out in a bid to minimize the structural weight of a wing with the lay-up thicknesses of wing components as design variabies. In addition, the effects of the power of spanwise variation function of lay-up thicknesses of skins and iugs on the optimized weights are also analyzed.

Multi-objective Optimization of a Two-Stage Helical Gearbox to Improve Efficiency and Reduce Height

The goal of this research is to look at multi-target optimization of a two-stage helical gearbox in order to determine the best key design elements for reducing gearbox height and enhancing gearbox efficiency.To do this,the method known as Taguchi and GRA(Grey Relation Analysis)were used in two stages to address the problem.The single-objective optimization problem was addressed first to close the gap between variable levels,and then the multi-objective optimization problem was solved to determine the best primary design variables.The first and second stage CWFWs(Coefficients of Wheel Face Width),ACS(Permissible Contact Stresses),and first stage gear ratio were also calculated.The study’s findings were utilized to identify the best values for five critical design aspects of a two-stage helical gearbox.

CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

OPERATION AND OPTIMIZATION OF BATCH DISTILLATION FOR A BINARY SYSTEM

Batch distillation,basically different from continuous distillation which is a steady stateprocess,appears to be an unsteady state process in its mathematical description.The theoreticalanalysis of its operation comprises a concomitant consideration of the stage-wise separation andthe equations of material balance as well as enthalpy balance.Based upon the batch distillationpractice of NMP-water system,this paper reveals the necessity and advantage of a computerizedtreatment for this purpose.Numerical results not only explain the experimental phenomena andprovide a design scheme,but also lead to the optimization of the operation condition.

Optimization of structural parameters for spatial flexible redundant manipulators with maximum ratio of load to mass

Optimization of structural parameters aimed at improving the load carrying capacity of spatial flexible redundant manipulators is presented in this paper. In order to increase the ratio of load to mass of robots, the cross-sectional parameters and constructional parameters are optimized respectively. The cross-sectional and configurational parameters are optimized simultaneously. The numerical simulation of a 4R spatial manipulator is performed. The results show that the load capacity of robots has been greatly improved through the optimization strategies proposed in this paper.

Stiffness Distribution and Aeroelastic Performance Optimization of High-Aspect-Ratio Wings

The relationship between stiffness distribution and aeroelastic performance for a beam-frame model and a3-D model is investigated based on aeroelastic optimization of global stiffness design for high-aspect-ratio wings.The sensitivity information of wing spanwise stiffness distribution with respect to the twist angle at wing tip,the vertical displacement at wing tip,and the flutter speed are obtained using a sensitivity method for both models.Then the relationship between stiffness distribution and aeroelastic performance is summarized to guide the design procedure.By using the genetic/sensitivity-based hybrid algorithm,an optimal solution satisfying the strength,aeroelastic and manufacturing constraints is obtained.It is found that the summarized guidance is well consistent with the optimal solution,thus providing a valuable design advice with efficiency.The study also shows that the aeroelastic-optimization-based global stiffness design procedure can obtain the optimal solution under multiple constraints with high efficiency and precision,thereby having a strong application value in engineering.

Global Optimization Algorithm for Nonlinear Sum of Ratios Problems

In this paper, a global optimization algorithm is proposed for nonlinear sum of ratios problem （P）. The algorithm works by globally solving problem （P1） that is equivalent to problem （P）, by utilizing linearization technique a linear relaxation programming of the （P1） is then obtained. The proposed algorithm is convergent to the global minimum of （P1） through the successive refinement of linear relaxation of the feasible region of objective function and solutions of a series of linear relaxation programming. Numerical results indicate that the proposed algorithm is feasible and can be used to globally solve nonlinear sum of ratios problems （P）.

Optimal Insurance with Background Risk under the Ambiguity and Belief Heterogeneity Structure

In this paper, we discuss the optimal insurance in the presence of background risk while the insured is ambiguity averse and there exists belief heterogeneity between the insured and the insurer. We give the optimal insurance contract when maxing the insured’s expected utility of his/her remaining wealth under the smooth ambiguity model and the heterogeneous belief form satisfying the MHR condition. We calculate the insurance premium by using generalized Wang’s premium and also introduce a series of stochastic orders proposed by [1] to describe the relationships among the insurable risk, background risk and ambiguity parameter. We obtain the deductible insurance is the optimal insurance while they meet specific dependence structures.

Optimization of Linear Antenna Arrays Based on Genetic Algorithms

The methods of moment and genetic algorithm (GA) are combined to optimize the Yagi Uda antenna array and Log periodic dipole antenna (LPDA) array. The element lengths and spacing are optimized for the Yagi Uda array; while the ratio factor of spacing to length as well as the ratio of length to diameter of the elements are optimized for LPDA array. The results show that the main parameters, such as gain and pattern, have been improved apparently; and the high back lobe level of LPDA can be reduced greatly, therefore, GA is a very competent method for optimizing the linear array as well as in other fields.

Identification of Maximum Road Friction Coefficient and Optimal Slip Ratio Based on Road Type Recognition

The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions（single-friction and joint-friction）by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.