Pareto Optimal Method Research Articles

Design parameter evaluation based on human operation for tip mechanism of forceps manipulator using surgical robot simulation

We proposed a design method for pediatric surgical robots that evaluates the workspace and view information in computer simulator before the actual robot is developed. In this study, we investigated a suturing task in a virtual environment using forceps manipulators with different mechanical parameters. We reproduced the surgical workspace for congenital esophageal atresia and measured the working volume and invisible area to obtain suitable parameters for the suturing task. We also calculated the suitable mechanical parameters using Pareto optimal solution method and verified the mechanical parameters in Pareto optimal solution. We verified from the experimental results that there is a trade-off between the working volume and invisible area during the suturing task. Moreover, we determined from the calculation results that the mechanical design of the forceps manipulator is influenced by the invisible area during the suturing task. Finally, we confirmed that it is possible to obtain suitable parameters for surgical robots using the proposed method.

Choosing the optimal addendum modification coefficient of external involute spur gear

The influence of addendum modification coefficient (AMC) on the durability and load-carrying ability of gear pairs is widely adopted in the production of gears. However, the effect of AMC only surveyed at single-point, intermittent in the range of values that have not come up with the optimal value of them. In order to overcome these disadvantages, this study is proposed to develop a method for choosing the optimal AMC of external involute spur gear with the aim to improve load-carrying capacity of gears, reduce vibrations, suppress the noises and extend service life and so on. This paper investigates choosing the optimal AMC of gears based on all the necessary conditions according to the standard design that is selected. Three objectives are studied for optimal selection. As such, the combination of Pareto optimal and genetic algorithm methods is applied for gear optimization. Our method is shown to be better in the difference maximum of sliding factors and the difference of form base on the maximum of sum of AMC. The analysis results of tooth root stress and contact stress were decreased significantly. This implies the feasibility of the optimization design for increasing the gears’ properties by choosing the optimal AMC.

An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants

In this paper, a stochastic model is proposed for planning the location and operation of Molten Carbonate Fuel Cell Power Plants (MCFCPPs) in distribution networks when used for Combined Heat, Power, and Hydrogen (CHPH) simultaneously. Uncertainties of electrical and thermal loads forecasting; the pressures of hydrogen, oxygen, and carbon dioxide imported to MCFCPPs; and the nominal temperature of MCFCPPs are considered using a scenario-based method. In the method, scenarios are generated using Roulette Wheel Mechanism (RWM) based on Probability Distribution Functions (PDF) of input random variables. Using this method, probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective functions, placement, and operation of MCFCPPs as CHPH change this problem to a mixed integer nonlinear one. So, multi-objective Modified Firefly Algorithm (MFA) and Pareto optimal method are employed for solving the multi-objective problem and for compromising between the objective functions. During the simulation process, a set of non-dominated solutions are stored in a repository. The 69-bus distribution system is used for evaluating the proper function of the proposed method.

Application of the ELECTRE method to planetary gear train optimization

This study applies multi-criteria optimization to a planetary gear train model. A planetary gear multi-criteria optimization model based on an original algorithm is presented. The experimentally determined approximations of analytical expressions for volume, mass, efficiency, and production costs serve as the basis of the algorithm. The following factors are adopted as optimization variables: numbers of teeth, number of planets, module, and face width. Functional constraints are expressed by conditions required for the proper functioning of the system in terms of geometry and strength. Apart fromthe set of Pareto optimal solutions and methods for choosing the optimal solution from this set, the ELECTRE method is included in the mathematical model. The program result analysis and comparison of all optimization methods are presented on the basis of the numerical examples obtained by applying developed software.

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal soluti...

成長階層型自己組織化マップを用いたパレート解集合の可視化

The visualization of the Pareto optimal solution set is one of important issues of the multi-objective optimization. The Pareto optimal solution visualization method using the self-organizing maps is one of promising visualization methods. This method has two shortcomings. One is that the map size has to be determined in advance. The other is that infeasible solutions can appear in the learnt maps. This paper proposes a new visualization technique using the growing hierarchical SOM (GHSOM), which is expected to solve foregoing shortcomings. This paper also proposes to introduce a symmetric transformation of maps into the learning algorithm in order to obtain easily viewable unified map. The effectiveness of the proposed method is confirmed through several numerical experiments.

Dynamic group job shop scheduling

This paper presents a dynamic Pareto-optimal method for flexible job shop scheduling based on two criteria simultaneously: the relative manufacturing cost criterion and the average orders utility criterion. Group scheduling for a given set of jobs with due dates that are typical for a ‘make-to-order’ strategy is considered. In this method the concept of production intensity as a dynamic production process parameter is used. A software package allows scheduling for a medium quantity of jobs. The result of software application is the set of non-dominant versions proposed to a user for making a final choice.

A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton–Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered.

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP) optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II) based on blood variation for above scheduling model. Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model. Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption. Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II) is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

Combined fitting of small- and wide-angle X-ray total scattering data from nanoparticles: benefits and issues

Simultaneous fitting of small- (SAS) and wide-angle (WAS) X-ray total scattering data for nanoparticles has been explored using both simulated and experimental signals. The nanoparticle types included core/shell metal and quantum-dot CdSe systems. Various combinations of reciprocal- and real-space representations of the scattering data have been considered. Incorporating SAS data into the fit consistently returned more accurate particle-size distribution parameters than those obtained by fitting the WAS data alone. A popular method for fitting the Fourier transform of the WAS data (i.e.a pair-distribution function), in which the omitted SAS part is represented using a parametric function, typically yielded significantly incorrect results. The Pareto optimization method combined with a genetic algorithm proved to be effective for simultaneous SAS/WAS analyses. An approach for identifying the most optimal solution from the Pareto set of solutions has been proposed.

Dynamic Pareto-optimal group scheduling for a single machine

This paper seeks to determine a dynamic Pareto-optimal method for a single machine scheduling based on two criteria simultaneously: setup expenditure criterion and average orders utility criterion. Series-batch and parallel-batch problems with sequence-dependent setup times are considered. In this method the concept of production intensity as dynamic production process parameter is used. A set of applied software allows scheduling for middle quantity of jobs. The result of software application is the set of non-dominant versions proposed to a user for making a final choice.

Dynamic Pareto-optimal group scheduling in parallel machine shop

This paper seeks to determine a dynamic Pareto-optimal method for parallel machine scheduling based on two criteria simultaneously: relative manufacturing cost criterion and average orders utility criterion. In this method, the concept of production intensity as a dynamic production process parameter is used. The method is applicable both for ‘make-to-order’ and ‘make-to-stock’ manufacturing strategies. A set of applied software allows scheduling for middle quantity of jobs. The result of software application is the set of non-dominant versions proposed to a user for making a final choice.

Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm

This paper presents a novel opposition-based self-adaptive modified gravitational search algorithm (OSAMGSA) for optimal reactive power dispatch and voltage control in power-system operation. The problem is formulated as a mixed integer, nonlinear optimization problem, which has both continuous and discrete control variables. In order to achieve the optimal value of loss, voltage deviation, and voltage stability index, it is necessary to find the optimal value of control variables such as the tap positions of tap changing transformers, generator voltages, and compensation capacitor. Therefore, this complicated problem needs to be solved by an accurate optimization algorithm. This paper solves the aforementioned problem by using the gravitational search algorithm (GSA), which is one of the novel optimization algorithms based on the gravity law and mass interactions. To improve the efficiency of this algorithm, the tuning of its parameters is accomplished using random generation, and by applying the self-adaptive parameter tuning scheme. Also, the proposed OSAMGSA of this paper employs the opposition-based population initialization and self-adaptive probabilistic learning approach for generation jumping and escaping from local optima. Since the proposed problem is a multiobjective optimization problem incorporating several solutions instead of one, we applied the Pareto optimal solution method in order to find all Pareto optimal solutions. Moreover, the fuzzy decision method is used for obtaining the best compromise solution between them.

A meshless Pareto-optimal method for topology optimization

Abstract A meshless Galerkin Pareto-optimal method is proposed for topology optimization of continuum structures in this paper. The compactly supported radial basis function (CSRBF) is used to create shape functions. The shape function is constructed by meshfree approximations based on a set of unstructured field nodes. Considering the Pareto-optimality theory, the initial single objective topology optimization problem is transformed into multi-objective problem. The optimum solution is traced via the Pareto-optimal frontier in a computationally effective manner. The optimal problem does not need to be solved directly. Finally, several examples are used to prove the validity and effectiveness of the proposed approach.

Simultaneous isocratic separation of phenolic acids and flavonoids using micellar liquid chromatography

The simultaneous isocratic separation of a mixture of five phenolic acids and four flavonoids (two important groups of natural polyphenolic compounds with very different polarities) was investigated in three different RPLC modes using a hydro-organic mobile phase, and mobile phases containing SDS at concentrations below and above the critical micellar concentration (submicellar LC and micellar LC (MLC), respectively). In the hydro-organic mode, methanol and acetonitrile; in the submicellar mode methanol; and in the micellar mode, methanol and 1-propanol were examined individually as organic modifiers. Regarding the other modes, MLC provided more appropriate resolutions and analysis time and was preferred for the separation of the selected compounds. Optimization of separation in MLC was performed using an interpretative approach for each alcohol. In this way, the retention of phenolic acids and flavonoids were modeled using the retention factors obtained from five different mobile phases, then the Pareto optimality method was applied to find the best compatibility between analysis time and quality of separation. The results of this study showed some promising advantages of MLC for the simultaneous separation of phenolic acids and flavonoids, including low consumption of organic solvent, good resolution, short analysis time, and no requirement of gradient elution.

Alternative Equations to Compute the Network and the Thermal Efficiency of the Irreversible Diesel Cycle Using Genetic Algorithm

In the current study, the effect of specific heat ratio, compression ratio, and the maximum to minimum temperature ratio of cycle on the thermal efficiency and the Network of irreversible Diesel cycle have been investigated. For this purpose, numerical solution and multi-objective genetic algorithms with a Pareto optimization method are used to determine the optimum values of specific heat ratio, compression ratio, and the maximum to minimum temperature ratio of cycle. The optimized values of the objective functions (namely Network and thermal efficiency) are then determined by using these vales. The numerical solution shows that the maximum Network and thermal efficiency is obtained simultaneously when the values of specific heat ratio, compression ratio, and the maximum to minimum temperature ratio of cycle are 1.3, 17.7, and 6.0, respectively, and these parameters are obtained by multi-objective genetic algorithms with a Pareto optimization method as 1.3, 17.57, and 6.0. The results obtained from current work are presented in the form of optimal equations for Network thermal efficiency in term of compression efficiency, expansion efficiency and compression ratio of the cycle. The results of current research work can provide a significant insight for optimal design of internal combustion engines including irreversibility.

Pareto optimality solution of the Gauss-Helmert model

The Pareto optimality method is applied to the parameter estimation of the Gauss-Helmert weighted 2D similarity transformation assuming that there are measurement errors and/or modeling inconsistencies. In some cases of parametric modeling, the residuals to be minimized can be expressed in different forms resulting in different values for the estimated parameters. Sometimes these objectives may compete in the Pareto sense, namely a small change in the parameters can result in an increase in one of the objectives on the one hand, and a decrease of the other objective on the other hand. In this study, the Pareto optimality approach was employed to find the optimal trade-off solution between the conflicting objectives and the results compared to those from ordinary least squares (OLS), total least squares (TLS) techniques and the least geometric mean deviation (LGMD) approach. The results indicate that the Pareto optimality can be considered as their generalization since the Pareto optimal solution produces a set of optimal parameters represented by the Pareto-set containing the solutions of these techniques (error models). From the Pareto-set, a single optimal solution can be selected on the basis of the decision maker’s criteria. The application of Pareto optimality needs nonlinear multi-objective optimization, which can be easily achieved in parallel via hybrid genetic algorithms built-in engineering software systems such as Matlab. A real-word problem is investigated to illustrate the effectiveness of this approach.

Multi-Objective Optimization of a Row of Film Cooling Holes Using an Evolutionary Algorithm and Surrogate Modeling

Multi-objective shape optimization of a row of laidback fan-shaped film cooling holes has been performed using a hybrid multi-objective evolutionary approach in order to achieve an acceptable compromise between two competing objectives: the enhancement of film cooling effectiveness and the reduction of aerodynamic loss. In order to perform comprehensive optimization of a film cooling hole shape, the injection angle of the hole, lateral expansion angle of the diffuser, forward expansion angle of the hole, and pitch-to-hole diameter ratio are chosen as design variables. Forty experimental designs within the design spaces are selected using the Latin hypercube sampling method. The response surface approximation method is used to construct the surrogate using objective function values calculated at the experimental points using Reynolds-averaged Navier-Stokes analysis. The shear stress transport turbulence model is used as a turbulence closure. The optimization results are processed using the Pareto-optimal method. The Pareto-optimal solutions are obtained using a combination of a evolutionary algorithm and a local search method. The optimum designs are grouped using the k-means clustering technique, and the three optimal points selected in the Pareto-optimal solutions are evaluated by numerical analysis. The optimum designs give enhanced objective function values compared to the experimental designs.

Chromatographic Behavior of Aromatic Diamines in Hydro-Organic, Micellar and Submicellar Reversed Phase Liquid Chromatographic Modes

The chromatographic behavior (retention, elution strength, efficiency, peak asymmetry and selectivity) of some aromatic diamines in the presence of methanol with or without anionic surfactant SDS in the four different reversed phased liquid chromatographic (RPLC) modes, i.e., hydro-organic, micellar (MLC), low submicellar (LSC) and high submicellar (HSC), was investigated. In the three surfactant-mediated modes, the surfactant monomers coat the stationary phase even up to 70 % methanol; this results in the suppression of peak tailing (by masking the silanol groups on the stationary phase). In MLC and HSC, the solute retention decreases by increasing the surfactant concentration, while this situation was reversed in LSC. In the region between MLC and HSC modes (25–50 % methanol), retention of late eluting solutes was increased by increasing methanol content which is seemingly due to disaggregation of SDS micelles. Changes in selectivity were observed after changing the concentrations of SDS and methanol, in a greater extent when concentration of SDS was changed. Among the four studied RPLC modes, HSC showed the best efficiency with nearly symmetrical peaks. Prediction of retention of solutes in HSC based on a mechanistic retention model combined with Pareto-optimality method allowed the full resolution of target diamines in practical analysis times.

On the Optimal Design of Risk Retention in Securitisation

This paper examines the optimal design of retention in securitisation, in order to maximize welfare of screening per unit of retention, assuming that screening is costly and that the bank intends to securitise its loans. In contrast to the focus of previous literature on tranche retention, we abstract from the constitutional mechanisms of tranche retention to present a pareto-optimal method of tranche retention. Unlike the current ad-hoc regulations, we derive the optimal design of retention from a utility maximisation problem. We show that the level of retention per tranche should be dependent on the rate of credit default, i.e. the higher the rate of default, the higher the optimal rate of retention required to provide an incentive to screen carefully. From this approach, it follows that the rate of retention per tranche should be higher, the higher the position within the ranking order of subordination. Accordingly, the efficiency of tranche retention can be enhanced, reducing the level of retention required to maintain a given level of screening-effort. This retention design entails a recovery of the bank’s equity capital, thereby increasing the bank’s credit availability.