Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.     

Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S_RO–C) and the normalized summation of rising time and overshoot of pendulum (S_RO–P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.     

Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm

Modern machining processes are now-a-days widely used by manufacturing industries in order to produce high quality precise and very complex products. These modern machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such processes is very important to satisfy all the conflicting objectives of the process. In this research work, a newly developed advanced algorithm named ‘teaching–learning-based optimization (TLBO) algorithm’ is applied for the process parameter optimization of selected modern machining processes. This algorithm is inspired by the teaching–learning process and it works on the effect of influence of a teacher on the output of learners in a class. The important modern machining processes identified for the process parameters optimization in this work are ultrasonic machining (USM), abrasive jet machining (AJM), and wire electrical discharge machining (WEDM) process. The examples considered for these processes were attempted previously by various researchers using different optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), harmony search (HS), shuffled frog leaping (SFL) etc. However, comparison between the results obtained by the proposed algorithm and those obtained by different optimization algorithms shows the better performance of the proposed algorithm.     

Multi-objective shape optimization using ant colony coupled computational fluid dynamics solver

An adaptation of a parametric ant colony optimization (ACO) to multi-objective optimization (MOO) is presented in this paper. In this algorithm (here onwards called MACO) the concept of MOO is achieved using the reference point (or goal vector) optimization strategy by applying scalarization. This method translates the multi-objective optimization problem to a single objective optimization problem. The ranking is done using ?-dominance with modified L_p metric strategy. The minimization of the maximum distance from the goal vector drives the solution close to the goal vector. A few validation test cases with multi-objectives have been demonstrated. MACO was found to out perform R-NSGA-II for the test cases considered. This algorithm was then integrated with a meshless computational fluid dynamics (CFD) solver to perform aerodynamic shape optimization of an airfoil. The algorithm was successful in reaching the optimum solutions near to the goal vector on one hand. On the other hand the algorithm converged to an optimum outside the boundary specified by the user for the control variables. These make MACO a good contender for multi-objective shape optimization problems.     

Modeling of deviation angle and performance losses in wet steam turbines using GMDH-type neural networks

In the present study group method of data handling (GMDH) type of artificial neural networks are used to model deviation angle (θ), total pressure loss coefficient (ω), and performance loss coefficient (ξ) in wet steam turbines. These parameters are modeled with respect to four input variables, i.e., stagnation pressure (P _z ), stagnation temperature (T _z ), back pressure (P _b), and inflow angle (β). The required input and output data to train the neural networks has been taken from numerical simulations. An AUSM–Van Leer hybrid scheme is used to solve two-phase transonic steam flow numerically. Based on results of the paper, GMDH-type neural networks can successfully model and predict deviation angle, total pressure loss coefficient, and performance loss coefficient in wet steam turbines. Absolute fraction of variance (R ²) and root-mean-squared error related to total pressure loss coefficient (ω) are equal to 0.992 and 0.002, respectively. Thus GMDH models have enough accuracy for turbomachinery applications.

     

一种改进的鲁棒多目标优化方法

针对在解决某些复杂多目标优化问题过程中,所得到的Pareto最优解易受设计参数或环境参数扰动的影响,引入了鲁棒的概念并提出一种改进的鲁棒多目标优化方法,它利用了经典的基于适应度函数期望和方差方法各自的优势,有效地将两种方法结合在一起。为了实现该方法,给出一种基于粒子群优化算法的多目标优化算法。仿真实例结果表明,所给出的方法能够得到更为鲁棒的Pareto最优解。     

一种基于多样性度量的多目标化进化算法

针对如何通过附加的方法对多目标化问题进行理论分析,提出并证明了选择附加函数的3个前提条件.提出一种多目标化进化算法,根据种群中个体的多样性度量进行多目标化,并采用改进的非劣分类遗传算法对构造所得的多目标优化问题进行多目标优化.在静态和动态两种环境下进行算法性能验证,结果表明,在种群多样性保持、处理欺骗问题、动态环境下的适应能力等方面,所提算法明显优于其他同类算法.     

Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making

An irreversible regenerative Brayton cycle model considering internal and external irreversibilities is developed in matrix laboratory (MATLAB) simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. Evolutionary algorithms based on second version of non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are employed to optimize power output and thermal efficiency simultaneously where isobaric-side heat exchanger effectiveness (ε_H), isothermal-side effectiveness (ε_H1), sink-side effectiveness (ε_L), regenerator-side effectiveness (ε_R), and working medium temperature (T₅) are taken as design variables. The optimal values of aforementioned design variables are investigated. Pareto optimal frontiers between dual objectives are obtained and the final optimal values of power output and thermal efficiency are chosen via LINMAP, fuzzy Bellman–Zadeh, Shannon’s entropy and TOPSIS decision making approaches. The obtained results are compared and the best one is preferred. An improvement in thermal efficiency from 18.29% to 21.10% is reported. In addition to this, variations of different input parameters on the power output and thermal efficiency are conferred and presented graphically. With the goal of error investigation, the maximum and average errors for the obtained results are designed at last.     

A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost

NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.     

Mixed GL2/H2/GH2 control with pole placement and its application to vehicle suspension systems

This paper presents a multi-channel multi-objective synthesis framework, in which generalized L ₂ (GL ₂), H ₂, and generalized H ₂ (GH ₂) criteria are specified for appropriate channels, and the poles of the closed-loop system are constrained within a subregion S(a, r,θ) of the left-half s-plane. The multi-objective framework is successfully applied to a seven degrees-of-freedom decoupled vehicle suspension system. The suspension control system has three channels: channel T ₁, relating to ride comfort, is from road disturbances to vehicle body accelerations, the suspension travel channel, T ₂, is from manoeuvre disturbances to suspension deflections, and channel T ₀ is used to address system uncertainty. The control objective is to minimize a mixture of ||T ₁|| _H2 and ||T ₂|| _GH2 while guaranteeing that the closed-loop system is robustly stable, with poles located within a specified subregion S(a, r,θ). The simulation results demonstrate that this multi-objective control can improve vehicle ride comfort and decrease suspension travel simultaneously. In addition, the multi-objective synthesis framework provides a simple but effective trade-off between vehicle ride comfort and suspension travel.     

Multi-objective optimization case study with active and passive design in building engineering

This research presents a synthetic case study for multi-objective optimization for an active and passive design procedure based on dynamic programming using genetic algorithms (GAs) and computational fluid dynamics (CFD). Both active and passive optimized variables are indispensable for efficient building design. This paper shows how to deal with these two different types of variables in the multi-objective optimization frame. Energy saving, thermal comfort, and indoor air quality are selected as objective functions. While demonstrating a synthetic multi-objective optimization with active and passive variables, this research analyzes the trade-off relation among objective functions in the indoor environment. In this research, representing fluctuating outdoor conditions as random variables, optimization of the building geometry as the passive design variable and an HVAC system as the active design variable was performed using the dynamic programming approach. This research consists of several tasks. First, multi-objective optimization is carried out by genetic algorithms and computational fluid dynamics, and then dynamic programming is applied to the control system with random variables.

     

A particle swarm approach for multi-objective optimization of electrical discharge machining process

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

基于多目标优化的网络社区发现方法

社区发现是复杂网络挖掘中的重要任务之一,在恐怖组织识别、蛋白质功能预测、舆情分析等方面具有重要的理论和应用价值.但是,现有的社区质量评判指标具有数据依赖性与耦合关联性,而且基于单一评判指标优化的网络社区发现算法有很大的局限性.针对这些问题,将网络社区发现问题形式化为多目标优化问题,提出了一种基于多目标粒子群优化的网络社区发现算法MOCD-PSO,它选取模块度Q、最小最大割MinMaxCut 与轮廓(silhouette)这3 个指标进行综合寻优.实验结果表明,MOCD-PSO 算法具有较好的收敛性,能够发现分布均匀且分散度较高的Pareto 最优网络社区结构集,并且无论与单目标优化方法(GN 与GA-Net)相比较,还是与多目标优化算法(MOGANet与SCAH-MOHSA)相比较,MOCD-PSO 算法都能在无先验信息的条件下挖掘出更高质量的网络社区.     

一种基于相角映射的改进多目标粒子群优化算法

提出一种相角粒子群优化算法求解多目标优化问题。该算法采用相角映射实现了粒子在相角空间上仅依赖于归一化多目标函数的快速搜索,在粒子飞行信息共享机制上引入共享池概念,提出基于关联支配排序和相似度排序的共享池更新策略,提高了Pareto解的多样性。采用Sigma领导策略和混沌变异操作,平衡了算法的快速搜索能力和全局寻优能力。标准多目标测试函数和电力系统广域阻尼控制多目标优化算例表明了所提出算法的可行性和有效性。     

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional–integral–derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K _p), integral gain (K _i) and derivative gain (K _d)] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

     

Geometric parameter optimization in multi-axis machining

This paper presents a systematic method for the determination of optimal geometric machining parameters in multi-axis machining. Machining accuracy is considered to be determined by a set of geometric parameters: the design parameters of the cutter, the positioning of the cutter, the orientation of the cutter etc. First, we formulate the general nonlinear constrained optimization model of the machining process. The optimal machining result is expected to produce the least deviation between the designed surface and the actual surface. This objective is accomplished by minimizing the deviation between the designed surface and the actual surface during machining. The details of how to characterize and calculate the deviation is then discussed for both ruled surface milling and general free-form surface milling. The swept surface is developed based on robotic manipulation and is used to model the actual surface. A signed distance function is constructed to perform the comparison which returns the signed distance from each sampled point to the designed surface. The direct search algorithm (Nelder-Mead simplex algorithm and pattern search algorithm in this paper) is used to solve our optimization problems due to possible discontinuity of the objective function and large nonlinearity of the problem. Three numerical examples and necessary comparisons are given to demonstrate the effectiveness of our method. The first example shows the generation of the swept volume of a filled-end cutter. The second example employs the swept surface generation method to solve a parameter optimization problem. Sensitivity analysis is performed for the parameters critical to machining accuracy. The third example optimizes the cutter orientation relative to the part surface to minimize the kinematics error caused by kinematics transformation and interpolation of multi-axis machines.     

Optimizing NC-tool paths for simultaneous five-axis milling based on multi-population multi-objective evolutionary algorithms

Computer-Numerical-Control based five-axis milling offers new possibilities for improving the machining process. However, this procedure is still difficult to handle, particularly in case of machining complex free-formed surfaces. An optimization approach based on the multi-objective evolutionary algorithm SMS-EMOA (S-metric selection evolutionary multi-objective optimization algorithm) combined with a multi-population approach has been developed and used in order to utilize the potential of the five-axis milling process. After a general introduction to this machining process and the potential of path optimization, the designed multi-population multi-objective evolutionary approach, its integration into the simulation, and its adaptation to the practical example is described.     

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR \((\hbox {R}_{\mathrm{a}})\) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.     

Software prototype for solving multi-objective machining optimization problems: Application in non-conventional machining processes

For an effective and efficient application of machining processes it is often necessary to consider more than one machining performance characteristics for the selection of optimal machining parameters. This implies the need to formulate and solve multi-objective optimization problems. In recent years, there has been an increasing trend of using meta-heuristic algorithms for solving multi-objective machining optimization problems. Although having the ability to efficiently handle highly non-linear, multi-dimensional and multi-modal optimization problems, meta-heuristic algorithms are plagued by numerous limitations as a consequence of their stochastic nature. To overcome some of these limitations in the machining optimization domain, a software prototype for solving multi-objective machining optimization problems was developed. The core of the developed software prototype is an algorithm based on exhaustive iterative search which guarantees the optimality of a determined solution in a given discrete search space. This approach is justified by a continual increase in computing power and memory size in recent years. To analyze the developed software prototype applicability and performance, four case studies dealing with multi-objective optimization problems of non-conventional machining processes were considered. Case studies are selected to cover different formulations of multi-objective optimization problems: optimization of one objective function while all the other are converted into constraints, optimization of a utility function which combines all objective functions and determination of a set of Pareto optimal solutions. In each case study optimization solutions that had been determined by past researchers using meta-heuristic algorithms were improved by using the developed software prototype.