Kinds Of Objective Functions Research Articles

Multi-objective optimization and fluid selection of organic Rankine cycle (ORC) system based on economic-environmental-sustainable analysis

Global issues such as the energy crisis and environmental pollution impulse the development of waste heat recovery technologies. Organic Rankine cycle (ORC) systems are a promising solution to utilize renewable energies and recover waste heat. However, the different heat source temperatures often force the ORC to use different working fluids. Matching the heat source temperatures with suitable fluids is important to enhance the system performance and promote the marketability of this technology. In this work, a double-layer multi-objective optimization framework is proposed for subcritical ORC systems applied in the geothermal field. Four kinds of objective functions are selected in the optimization model, including net power output, total product unit cost, greenhouse gas emissions, and ecological life cycle cost to characterize system thermodynamic, exergoeconomic, environmental, and sustainable performances, respectively. The feature of the established model allows simultaneous system comprehensive design and fluid screening under specific geothermal temperatures. Results showed that the impacts of decision variables, including evaporation pressure and condensation temperature, on system performances were different under the different performance indicators. According to the balanced weighting factor case study, it is found that R134a had excellent thermodynamic and sustainable performances while R600a performed better from economic and environmental aspects at 393.15 K geothermal temperature. In addition, it was observed that the obtained optimal point shifted regularly on the Pareto curve with the change of geothermal temperature and weighting factor. Finally, under different weighting factor schemes, the optimal plans of fluid selection were obtained at certain geothermal temperatures. HCs fluids showed superior overall performance in the double-layer optimization framework considering ecological impacts, while R152a exhibited excellent comprehensive performance among the selected safe fluids under the premise of strict consideration of system security.

Multi-objective optimization of PID controller of outlet temperature of DSG trough solar collector

In order to improve the quality of outlet temperature of DSG trough solar collector, the classical multi-objective optimization method is used to optimize the parameters of PID controller. In the optimization process, two kinds of objective functions are designed, one kind is the direct objective functions, the other kind is the indirect functions. The simulation curves of the parameters obtained by the multi-objective optimization method are much better than those of the original literature in the adjustment time and other indicators. In addition, the control valve position changes are within the allowable range, and the control system also has a certain robustness. Finally, the solution time of indirect objective function is about 1/6 of that of direct objective function, and there is no need to determine the ideal value of control index. Therefore, it is reasonable and effective to use multi-objective optimization algorithm and indirect objective function to optimize the parameters of PID controller for the outlet temperature of trough solar collector.

A pragmatic investigation of the objective function for subsurface data assimilation problem

One of the main mechanisms of an optimization problem is the effectiveness and relevance of the objective function. In the context of an optimization problem in the subsurface domain, called seismic history matching, this study proposes to investigate further aspects of assimilating data. We focus on two main characteristics of the objective function: the influence and the sensitivity to the amount of data used in the seismic history matching. We select four metrics to analyse the similarity/dissimilarity measurement used in the matching. The optimization method used to perform the seismic history matching is an auto-adaptive differential evolution algorithm. This study has been carried out on three real datasets. Based on the results and analysis of the seismic history matching experiments, we are able to draw some practical suggestions on what kind of objective function should be established. Despite its simplicity, the Least Square metric performs as well as any other metric. Using all the possible data is safer but it is not compulsory to obtain good history matching results, in some cases using less data leads to the same answer. Using different metrics or more data does not change the computing time.

Surge Margin Optimization of Centrifugal Compressors Using a New Objective Function Based on Local Flow Parameters

Currently, 3D-CFD design optimization of centrifugal compressors in terms of the surge margin is one major unresolved issue. On that account, this paper introduces a new kind of objective function. The objective function is based on local flow parameters present at the design point of the centrifugal compressor. A centrifugal compressor with a vaned diffuser is considered to demonstrate the performance of this approach. By means of a variation of the beta angle distribution of the impeller and diffuser blade, 73 design variations are generated, and several local flow parameters are evaluated. Finally, the most promising flow parameter is transferred into an objective function, and an optimization is carried out. It is shown that the new approach delivers similar results as a comparable optimization with a classic objective function using two operating points for surge margin estimation, but with less computational effort since no second operating point near the surge needs to be considered.

Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 1: Method and synthetic examples

When reliable a priori information is not available, it is difficult to correctly predict near-surface S-wave velocity models from Rayleigh waves through existing techniques, especially in the case of complex geology. To tackle this issue, we have developed a new method: two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI). Adopting a two-grid parameterization of the model, the genetic algorithm inverts for unknown velocities and densities at the nodes of a coarse grid, whereas the forward modeling is performed on a fine grid to avoid numerical dispersion. A bilinear interpolation brings the coarse-grid results into the fine-grid models. The coarse inversion grid allows for a significant reduction in the computing time required by the genetic algorithm to converge. With a coarser grid, there are fewer unknowns and less required computing time, at the expense of the model resolution. To further increase efficiency, our inversion code can perform the optimization using an offset-marching strategy and/or a frequency-marching strategy that can make use of different kinds of objective functions and allows for parallel computing. We illustrate the effect of our inversion method using three synthetic examples with rather complex near-surface models. Although no a priori information was used in all three tests, the long-wavelength structures of the reference models were fairly predicted, and satisfactory matches between “observed” and predicted data were achieved. The fair predictions of the reference models suggest that the final models estimated by our genetic-algorithm FWI, which we call macromodels, would be suitable inputs to gradient-based Rayleigh-wave FWI for further refinement. We also explored other issues related to the practical use of the method in different work and explored applications of the method to field data.

A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Dynamic Monitoring and Forecasting of the Soundness of U.S. Insurers in a Cyclical Environment

Abstract This paper presents a model of dynamic monitoring and forecasting of key financial indices of U.S. insurers. The key financial indices are assumed to be cyclically time-varying correlated and are selected according to their impact on the soundness of the insurers. It also presents a new kind of control chart, μ r {\mu _r} chart, based on the weighted average of standardized financial indices. Three kinds of objective functions are applied to determine the optimal weights: (1) minimizing the probability of a missed alarm; (2) minimizing the volatility of the weighted average of standardized financial indices; and (3) minimizing the expected shortfall of the weighted average of standardized financial indices. We note that the optimal weights are equal weights no matter which objective function is selected. The control technique presented in this paper can be extended to monitor the soundness of other insurance firms in a cyclical environment.

Инженерный метод моделирования и оптимизации химико-технологических процессов

In the proposed work, the engineering method of modeling and optimization will be described using the example of tire development and rubber products. A new kind of objective function is proposed that allows to determine almost completely optimal technological process parameters (including properties of raw materials and raw materials) taking into account the robustness. Examples of application of the proposed method are given.

A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization

Endmember extraction is one of the most important issues in hyperspectral image analysis. Under the linear mixing model and pure pixel assumption, a number of convex-geometry-based methods have been developed in the past decades. However, these traditional methods generally produce unsatisfactory results since they require the hyperspectral image to have a convex structure and this is not exactly the case with the real image scene. The particle swarm optimization (PSO) algorithm has recently been employed to address the endmember extraction problem, but its performance is limited by the premature convergence and lower precision of the standard PSO, and much effort is required to enhance the optimization result. To address these problems, in this study, a novel quantum-behaved particle swarm optimization (QPSO) algorithm is proposed for hyperspectral endmember extraction. The notable advantages of the proposed method include: 1) a row–column coding approach for the particles is designed to accelerate the optimization process; 2) a cooperative approach is employed to update the particles’ individual and global best positions, in order to help the particles’ optimization behavior in the multidimensional search space; and 3) two kinds of objective functions, namely, maximizing the simplex volume formed by the endmember combination, and minimizing the root-mean-square error between the original image and its remixed image, are incorporated in a sequential optimization approach for the endmember extraction problem, which makes the algorithm robust to outliers at an acceptable time cost. The extensive experimental results prove that QPSO is able to find the optimal endmember combination.

Train re-scheduling based on an improved fuzzy linear programming model

Purpose – Train re-scheduling remains a longstanding challenge in railway operation. To design high-quality timetable in fuzzy environment, the purpose of this paper is to study train re-scheduling problem under the fuzzy environment, in which the fuzzy coefficients of the constraint resources have the fuzzy boundaries. Design/methodology/approach – Based on the improved fuzzy linear programming, the train re-scheduling model is constructed. Aiming at dealing with the fuzzy characteristics of the constraint coefficients value range boundaries, the description method of this kind of objective function is proposed and the solving approach is presented. The model has more adaptability to model a common train re-scheduling problem, in which some resources of the constraints are uncertain and have the characteristics of fuzziness and the boundaries of the resources are fuzzy. Findings – Two numerical examples are carried out and it shows that the model proposed in this paper can describe the train re-scheduling problem precisely, dealing with the fuzzy boundaries of the fuzzy coefficients of the constraint resources. And the algorithm present is suitable to solve the problem. The approach proposed in this paper can be a reference for developers of railway dispatching system. Originality/value – It is the first time to study train re-scheduling problem under the fuzzy environment, in which the fuzzy coefficients of the constraint resources have the fuzzy boundaries.

Velocity Vector Field Optimization in Bioventing

AbstractBioventing is a technology used to remove some kinds of pollutants from the subsoil and it is based on the capability of some bacteria species to biodegrade contaminants. The biochemical reaction requires, among other things, oxygen and, therefore, oxygen is inflated into the subsoil by wells. The mathematical model describes the movement of the different fluids which are present in the subsoil – air, water, pollutants, oxygen and so on – and the bacteria population dynamics. The presence of source reactive terms in the continuity equations allows the contaminant biodegradation to be described.The design of a subsoil decontamination intervention concerns bioavailability problems and, in particular, the oxygen concentration. Therefore, in order to enhance the biodegradation phenomenon, the optimization of the subsoil oxygen velocity field in the polluted area is required, by an appropriate choice of the well positions and of the well air inflating rates. In mathematical terms, the goal is to obtain the decontamination of the subsoil with an optimal value of an objective function by acting on some control variables which, in this case, are the well positions and the inflating rates. In this paper several kind of objective function are proposed. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Topology optimum design of compliant mechanisms using modified ant colony optimization

A Modified ant colony optimization (MACO) algorithm was suggested for topology optimal design of compliant mechanisms since standard ACO cannot provide an appropriate optimal topology. In order to improve computational efficiency and suitability of standard ACO algorithm in topology optimization for compliant mechanisms, a continuous variable, called the “Element contribution significance (ECS),” is employed, which serves to replace the positions of ants in the standard ACO algorithm, and assess the importance of each element in the optimization process. MACO algorithm was applied to topology optimizations of both linear and geometrically nonlinear compliant mechanisms using three kinds of objective functions, and optimized topologies were compared each other. From the comparisons, it was concluded that MACO algorithm can effectively be applied to topology optimizations of linear and geometrically nonlinear compliant mechanisms, and the ratio of Mutual potential energy (MPE) to Strain energy (SE) type of objective function is the best for topology optimal design of compliant mechanisms.

A Mathematical Programming Formulation for Cost-oriented Multi-manned Assembly Line Balancing Problem

Multi-manned assembly line balancing problems are usually occurred in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost- oriented objective function is presented for a multi-manned assembly line balancing problem. This kind of objective function may be used to balance final assembly lines of products in which manufacturing process is very labor intensive. A mixed-integer mathematical programming model is proposed to solve the problem optimally. The proposed formulation has been used to solve some small size problems by considering both time-oriented and cost-oriented objective functions. The experiments show that, given the same precedence graph of multi-manned assembly line with a same cycle time, two different optimal solutions can be actually found when switching from time-oriented to cost-oriented objective functions, and vice versa. This difficulty increases the complexity of the cost-oriented multi-manned assembly line balancing problems with respect to the multi-manned assembly line balancing problems addressed in the literature.

Construction and comparison of multi-model and mixed-model assembly lines balancing problems with bi-objective

Mass customization makes more important for assembly line balancing problems. In a large number of real-world problems, production managers face multiple, often conflicting, decision criteria. Accordingly, mixed-model and multi-model of assembly lines balancing problems (MiALBP and MuALBP) with bi-objective are considered in this paper. Firstly, two efficient mathematical programming models, MiALBP and MuALBP with bi-objective, are constructed. The first priority objective is to minimize total cycle time under a given number of workstations, and the second priority objective is related to horizontal balancing. Three kinds of objective functions are used to represent the degree of horizontal balancing. Two examples are chosen as the basis for the comparisons of these two models. The computational results of total cycle time minimization indicate that MiALBP model is superior to MuALBP one. However, MuALBP is better than MiALBP in horizontal balancing under given total cycle time.

Partial Rescheduling Based on Branch and Bound for Single-Machine Problem

This paper addresses a single-machine large-scale rescheduling problems with efficiency and stability subject to machine breakdown. Partial rescheduling (PR) strategy is used to cope with the computational complexity. Two kinds of objective functions of PR sub-problem, where the global dual objectives are reflected to an extent, are designed respectively for the procedural PR horizon and the terminal PR horizon. The PR problem is solved by a branch and bound algorithm. Lower bound and upper bound procedures as well as dominance rules are developed for the branch and bound algorithm. An extensive experimentation was conducted. The computational results show that the branch and bound can solve PR sub-problems with certain scales and the partial rescheduling procedure developed can greatly improve the stability of schedule with little sacrifice in efficiency and provide a reasonable trade-off between solution quality and computational cost.

Minimizing the weighted sum of maximum earliness and maximum tardiness costs on a single machine with periodic preventive maintenance

We consider the problem of scheduling a set of jobs on a single machine against a common and restrictive due date. In particular, we are interested in the problem of minimizing the weighted sum of maximum earliness and maximum tardiness costs. This kind of objective function is related to the just-in-time environment where penalties, such as storage cost and additional charges for late delivery, should be avoided. First we present a mixed integer linear model for the problem without availability constraints and we prove that this model can be reduced to a polynomial-time model. Secondly, we suppose that the machine undergoes a periodic preventive maintenance. We present then a second mixed integer linear model to solve the problem to optimality. Although the latter problem can be solved to optimality for small instances, we show that the problem reduces to the one-dimensional bin packing problem. Computational results show that the proposed algorithm best fit decreasing performs well.

Constructing Core Backbone Grid Based on the Index System of Power Grid Survivability and BBO Algorithm

Constructing core backbone grid is conducive to strengthen the construction of grid structure and improve the ability of withstanding natural disasters. The survivability index of power grid is made up of four indices, namely, the resistibility, recoverability, security, and connectivity. Based on survivability, a method of constructing core backbone grid with the optimal criteria of the minimal total line length and the largest integrated survivability index is put forward. The biogeography-based optimization (BBO) algorithm is used to search for the core backbone grid. Compared with the traditional algorithms, BBO algorithm shows advantages in fast speed of convergence and high convergence precision. Moreover, the searching results for three kinds of objective functions by BBO algorithm verify the effectiveness of the proposed model of constructing core backbone grid.

Balance de Flujos Metabólicos en Saccharomyces cerevisiae basado en Compartimentalización Intracelular

Flux Balance Analysis - FBA - is one of the most used techniques in prediction of microorganism bioproducts. It requires an objective function that represents biological objective of the studied microorganism. This paper presents a new kind of objective functions based on individual physical compartment objetives in the studied microorganism. These kind of functions was tested with a stoichiometric model extracted from iMM904 reconstruction of S. cerevisiae and its performance is compared with the most used objective function in literature, growth maximization, in anaerobic and aerobic batch conditions. The presented objective function outperform growth predictions in 10% and ethanol predictions in 75% compared with obtained by maximization of growth objective function, in anaerobic conditions. In aerobic batch conditions the presented objective function outperforms in 98% growth preditions and 70% ethanol predictions compared with growth maximization.

THE SERVICE REQUIRED QUALITY ENSURE MODEL OF LTE TECHNOLOGY DOWNLINK

Article presents a mathematical model of resource blocks distribution in LTE technology downlink. Offered model aims at providing guaranteed quality of service wireless users by user stations allocating of required transmission rates. Comparative analysis of obtained solutions by using different objective functions. Using various kinds of objective functions in offered model is directed to resource blocks allocation of minimum downlink or a minimum data transmission rate of each user station. It was found the greatest savings of time frequency resource allows objective function aimed at minimizing number of used resource blocks. The model offered in classes of service CoS corresponds to Class B each providing each user station a guaranteed transmission rate, with access to additional (nonguaranteed) bandwidth

Particle Swarm Optimization for Ship Degaussing Coils Calibration

In this paper, we deal with the problem of the ship degaussing coils optimal calibration by a linearly decreasing weight particle swarm optimization (LDW-PSO). Taking the ship’s magnetic field and its gradient reduction into account, the problem is treated as a multi-objective optimization problem. First a set of scale factors are calculated by LDW-PSO to scale the two kinds of objective function, then the multi-objective optimization problem is transformed to a single objective optimization problem via a set of proper weights, and the problem is solved by LDW-PSO finally. A typical ship degaussing system is applied to test the method’s validity, and the results are good.