Pareto Optimal Method Research Articles

Trade-off between the economic and environmental impact of different decarbonisation strategies for residential buildings

German residential multi-family buildings in the 1950s–1970s age group are a relevant case study to assess the degree of certainty in meeting climate targets for the built environment. This research evaluates the contribution of traditional strategies, and whether innovative strategies perform environmentally and economically better for this purpose. We use LCA to define a benchmark for the environmental impact, expressed as lifecycle GWP, and LCC to assess the economic impact, expressed as Internal Rate of Return (IRR). The two indicators are evaluated in a Pareto optimal method. We consider combinations of strategies for the thermal envelope, the building services, and the integration of locally available renewable energy in five existing buildings. Results show that accelerating the implementation rate of traditional strategies contributes only partially to raise the degree of certainty in reaching climate targets. For this purpose, innovative building services strategies as well as the extensive integration of local energy generation from renewable sources is required, even if the GWP reduction target of electricity from the main grid is met. The use of renewable energy has a much bigger impact on the overall GWP balance than the choice between a standard renovation and a minimal renovation of the building envelope. IRR tends to sink with GWP. We conclude that a regulated benchmark for the environmental lifecycle impact of residential building projects will help to introduce the trade-off assessment necessary to identify economically sustainable strategies that increase the level of certainty in meeting climate targets.

Numerical methodology on virtual model extension and system-level optimization of light-duty diesel vehicle with dual-loop exhaust gas recirculation

Because of the increased need for virtual analysis during the vehicle manufacturing processes, more stringent optimization methods are required for the simulation field. Owing to the use of big data from engine testing, 1D analysis can provide more powerful approaches in the conceptual design phase for car makers. In this study, system-level optimization of dual-loop EGR was performed at both the engine and vehicle level, whereas our previous research had been performed at the engine level. Depending on the virtually developed engine and vehicle models and control scheme from our previous works, improved driving capabilities could be observed under light-duty diesel vehicle systems under the world-harmonized light-vehicles test procedure (WLTP). The numerical model was extended through two steps. The first step includes model conversion from the base engine model with an HP EGR system to a virtual engine model with a dual-loop EGR system. The second step represents mode extension from the virtual engine model with a dual-loop EGR system to a vehicle model with a dual-loop EGR system. Optimizing the dominant parameters and using design of experiment (DoE)-based multi-objective Pareto optimization methods in each step, fuel economy could be improved by approximately, 1.5% and the deNOX rate was approximately 5% that of the conventional NEDC. It is implied that the dual-loop EGR system and gear strategy could improve vehicle performance under difficult driving conditions.

A fuzzified Pareto multiobjective cuckoo search algorithm for power losses minimization incorporating SVC

This paper presents an application of multiobjective cuckoo search (MOCS) algorithm for reduction in transmission line losses by placing static VAR compensator (SVC) at an optimal location. MOCS algorithm is an extension of the infamous cuckoo search algorithm. The multiobjective optimizations considered in this paper include active power loss and reactive power loss, active power loss and investment cost of SVC. The Pareto-optimal solution which is obtained by using the Pareto-optimal method gives the solution to the multiobjective problem. The fuzzy logic approach is used to obtain best trade-off solutions from the Pareto-optimal solution. A standard IEEE 30 bus test system is considered for testing the efficacy of the proposed methodology. The results show that installation of SVC and application of MOCS algorithm is effective in power loss reduction.

Specialized finite elements for numerical simulation of the flexibly-reconfigurable roll forming process

Flexibly-reconfigurable roll forming (FRRF) process is suggested in recent years as a response to the needs of modern manufacturing industries for small-lot or even single-lot production of doubly-curved sheet metal surfaces with reduced costs. In FRRF, a sheet metal is deformed by the use of two bent rollers with non-constant roll gap where the rolling and roll-bending mechanisms are imposed together. Thanks to FRRF process, the costs due to the manufacturing of dies are reduced; however, the computational cost for process design becomes significant as each design may be used for a few or even a single production. Hence, the net production rate is controlled by computational time that can be several times longer than the forming time. Considering the deformation mechanism in FRRF, this study tries to specialize an analysis method to FRRF that is accurate and efficient. This is accomplished by an in-house Matlab implementation of an efficient finite element approach and testing various combinations of elements and variational formulations (mixed and irreducible) to find the best combination of them for numerical simulation of FRRF. For this purpose, first a set of preliminary comparisons are made to select: (1) the best integration method for the elements with irreducible formulation, (2) the best stress components to be included in the nodal parameters for the elements with mixed formulation, and (3) the best elemental class (Serendipity or Lagrangian) for the elements with nonlinear interpolation in at least two directions. Then, the best variational formulation is decided for each elemental family by comparing the simulation results in a range of numerical model parameters. Finally, the FE model parameters are optimized for each candidate by a surrogate-based Pareto optimization method, and the best element/formulation couple is selected among all the candidates by the technique for order of preference by similarity to ideal solution (TOPSIS). Moreover, in order to identify the most decisive parameters in the analysis and design of FRRF process, the response of FRRF to variation of several process parameters including thickness, width, rollers' curvature radius, roll gap distribution, and material properties is investigated and the results are compared with those reported by the other studies.

A Multi-Attribute Expansion Planning Model for Integrated Gas–Electricity System

Gas-fired power plants are environmentally friendly because of their high efficiency rates and low CO2 emissions. On the other hand, the output power of renewable generators is stochastic, meaning that additional capacity must be held in reserve throughout the system. Gas-fired power plants are ideally suited to mitigate renewable uncertainties as they are more flexible and can easily be fired up in just a few minutes, and subsequently be shut down. Increased use of gas-fired power plants makes gas and electricity networks more dependent, so that adequacy in fuel supply of electricity network becomes a majority. However expansion planning of gas and electricity systems is accomplished by private gas and electricity companies, having no effective data exchange mechanism together. So there is a need to provide a model that coordinates the expansion planning of gas and electricity networks. On the other hand, expansion cost of either gas or electricity network and risk criteria of integrated energy system may have priority in decision-making process. With different challenging attributes, there is a gap in the literature to provide a model that takes into account the privacy of energy parties with a minimum data exchange, while considering different attributes in decision-making process. In this paper a multi-attribute decision-making (MADM) method for co-expansion planning of gas and electricity systems is introduced. The proposed MADM method supposes that a central entity as Ministry of Energy (ME) is responsible for coordinated expansion planning of gas and electricity networks, while taking into account the privacy of gas and electricity energy parties. Decision-making attributes are conflicting and the proposed method selects the best plan based on a compromise among the attributes. Different attributes including gas expansion cost (GEC), electricity expansion cost (EEC), minimum of maximum regret (MMR) and β-robustness (β_R) are considered to find the best plan with regard to the preferences of independent gas and electricity network operators. In this regard, two multi-attribute decision analysis methodologies are employed: analytical hierarchy process (AHP) is used as a simple way to weight and rank all the attributes objectively and find the relative importance of various plans, and the weighted sum method to provide a general composite index and finding the final appropriate plan. A real case study in the Khorasan province of Iran, which has a high penetration level of gas-consuming generation units (GCGU), is utilized to demonstrate the effectiveness of proposed MADM method. Results are compared with a Pareto optimal method to qualify the accuracy of proposed method.

The Reactor Size Influence on the Optimal Operating Setpoint Choice for a Fixed-Bed Multi-Tubular Catalytic Reactor

Optimal operation of chemical reactors of high thermal sensitivity is a central engineering problem of very high current interest. One elegant alternative to choose the optimal setpoint when at least two contrary (opposite) objectives are considered is based on the so-called Pareto-optimal front technique. This paper exemplifies how to generate Pareto optimal operating policies when reactor productivity and safety objectives (expressed in probabilistic terms) are simultaneously considered in the presence of technological constraints, uncertainty in safety boundaries, and random fluctuations in control variables. Beside the operating control variables, one important design variable is the reactor pipe diameter because it is directly related to the reaction heat removal. This paper exemplifies the influence of this design variable on the setpoint choice when applying the Pareto-optimal front method with computing the runaway-boundaries by using the generalized sensitivity criterion of Morbidelli and Varma (MV-criterion). An example is provided for an industrial fixed-bed tubular reactor used for the catalytic oxidation of benzene to maleic anhydride (MA) in vapour phase.

Optimal Multi-objective Placement of Wind Turbines Considering Voltage Stability, Total Loss and Cost Using Fuzzy Adaptive Modified Particle Swarm Optimization Algorithm

The proper placement of distributed generations, especially wind turbines, is a challenging issue in distribution networks. In this regard, this paper employs the fuzzy adaptive modified particle swarm optimization (FAMPSO) to determine the locations of wind turbines in a radial distribution network by considering power losses, operation cost reduction and voltage stability improvement as the objective functions. Considering the nature of these objective functions and load flow necessity, wind turbine placement is a nonlinear and complicated numerical problem. Therefore, the multi-objective FAMPSO and Pareto optimal methods are employed for compromising between the objective functions. Moreover, during the simulation procedure, a set of non-dominated solutions is stored in an external memory. This method is applied to a 69-bus distribution network for algorithm verification.

Pareto optimization-based trade-off between the visual resolvability and the intersection space of view of binocular microscopic vision system for microassembly systems

Visual resolvability and intersection space of view of a binocular microscopic vision system (MVS) in microassembly systems have influences on each other via analyzing theoretically. According to requirements of various microassembly tasks, it is an explicit problem how to find the best compromise between the visual resolvability of binocular MVS and its intersection space of view in three-dimensional space. A Pareto optimization-based method to make the trade-off between the visual resolvability and the intersection space of view of binocular MVS with relevant fixed parameters in microassembly systems is proposed. Through modeling and analyzing on the visual resolvability and intersection space of view of a binocular MVS, the topology and optical magnifications of two MVSs are considered as the key parameters to optimize its physical properties. A multiobjection optimization model based on a Pareto optimization method is formulated and used to find the Pareto optimal cure, namely the Pareto front, which represents the best trade-off between the visual resolvability and the intersection space of view of a binocular MVS. On these bases, the physical properties of a binocular MVS with structural parameters corresponding to the optimization solutions are numerically simulated. Accordingly, a binocular MVS with different parameters is established on an automatic microassembly system with a single manipulator with five degree-of-freedom and experimentally tested. The simulation and experimental results indicate that all of the optimal solutions, which locate on the Pareto front, are the best trade-off between the visual resolvability and the intersection space of view of a binocular MVS. According to the Pareto front, physical parameters of a binocular MVS can be chosen to get the optimal visual resolvability with a certain intersection space of view and vice versa.

Multi QoS constrained data sharing using hybridized pareto-glowworm swarm optimization

Wireless sensor network (WSN) is the group of sensor nodes which attempts to monitor and share the data. Secured and reliable routing in the WSN is the most arduous task which needs to be performed with more concern for the stable output. In the existing work clustering based symbiotic organism search method is used to share the data with assured reliability and packet delivery ratio. However, this approach lacks performance due to the intruder, and it is resolved in the proposed method by introducing the novel method namely hybridized pareto-glowworm swarm optimization and authenticated data communication. In this paper, cluster head selection is made by considering multiple quality of service parameters such mobility and energy whereas in the previous research only energy is considered. To select the optimal cluster head with multiple objectives, a pareto optimal method is hybridized with the GSO algorithm for the multi-objective fitness evaluation. Here formation of clusters is purely based on the selected cluster Head. After clustering, data is gathered from the multiple sensor nodes which are aggregated to reduce the bandwidth utilization and forwarded to the base station. To ensure the security level in this work authentication between the cluster head and the base station is done and then data is transmitted. The experimental results concluded that the proposed method has a better result than the existing methods.

Application of Pareto optimization method for ontology matching in nuclear reactor domain

Abstract This article describes the need for ontology matching and describes the methods to achieve the same. Efforts are put in the implementation of the semantic web based knowledge management system for nuclear domain which necessitated use of the methods for development of ontology matching. In order to exchange information in a distributed environment, ontology mapping has been used. The constraints in matching the ontology are also discussed. Pareto based ontology matching algorithm is used to find the similarity between two ontologies in the nuclear reactor domain. Algorithms like Jaro Winkler distance, Needleman Wunsch algorithm, Bigram, Kull Back and Cosine divergence are employed to demonstrate ontology matching. A case study was carried out to analysis the ontology matching in diversity in the nuclear reactor domain and same was illustrated.

Integrated stochastic optimization approaches for tactical scheduling of trains and railway infrastructure maintenance

Abstract This work addresses a tactical railway traffic scheduling problem focused on the optimization of train sequencing and routing decisions and timing decisions related to short-term maintenance works in a railway network subject to disturbed process times. This is modeled as a mixed-integer linear programming formulation in which the traffic flow and track maintenance variables, constraints and objectives are integrated under a stochastic environment. The resulting bi-objective optimization problem is to minimize the deviation from a scheduled plan and to maximize the number of aggregated maintenance works under stochastic disturbances. The two objectives require to schedule competitive train operations versus maintenance works on the same infrastructure elements. Computational experiments are performed on a realistic railway network. We measure the quality of the integrated solutions in terms of their robustness to stochastic perturbations of the train travel times and of the maintenance works. Pareto optimal methods are compared for the bi-objective problem. We also evaluate the impact of introducing routing stability constraints in order to force the trains to keep the same route among the different stochastic disturbed scenarios. The experiments show that forcing the routing stability reduces the routing flexibility and the ability to optimize the two performance indicators when dealing with stochastic disturbances.

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

Improving network security is a difficult problem that requires balancing several goals, such as defense cost and need for network efficiency, in order to achieve proper results. In this paper, we devise method of modeling network attack in a zero-sum multi-objective game and attempt to find the best defense against such an attack. We combined Pareto optimization and Q-learning methods to determine the most harmful attacks and consequently to find the best defense against those attacks. The results should help network administrators in search of a hands-on method of improving network security.

Searching for the Pareto frontier in multi-objective protein design.

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Allocation of emission permits for China’s power plants: A systemic Pareto optimal method

Allocation of emission permits (AEP) is an important issue because of its significant effects on environmental governance and operations management. However, whether AEP results can constantly maintain systemic Pareto optimality still remains unclear and has not been investigated adequately in prior literature. We attempt to fill this gap by dividing the AEP process into a pre-stage observation process and a two-stage regulatory scheme as motivated by recent real-world examples. We apply the characterizations of each stage’s state variables to build a conceptual AEP model based on the classical theory of data envelopment analysis (DEA). Considering different real-world scenarios, we extend this conceptual AEP model into three different AEP models: non-limited, uniform-limited, and heterogeneous-limited AEP models. The allocation schemes derived from each of these three models are proved theoretically to be systemic Pareto optimal in the corresponding scenarios. The advantages of our models over other AEP methods are real-world tractability, enforceability, and systemic Pareto optimality. We further conduct an empirical analysis on allocating SO2 emission permits among mainland China’s major million-KW coal-fired power plants using the proposed models. Results of our empirical study show that the heterogeneous-limited AEP model exhibits higher performance over the non-limited and uniform-limited AEP models. Thus, we suggest that the Chinese coal-fired power industry should employ the heterogeneous-limited AEP model in the practical allocation of SO2 emission permits.

A technology-based analysis of the water-energy-emission nexus of China’s steel industry

Steel production is a main water consumer, energy consumer, and a major source of air pollution in China. To mitigate the steel industry’s environmental pressures, the government is promoting several water-saving, energy-saving, and emission-reduction technologies. This study developed a technology-based approach to investigate the water, energy and emission nexus (WEEN) of the steel industry. The results indicate that 4.1m3 of water use and 21.4kWh of electricity use were related with the WEEN per ton of crude steel production in 2014, which accounted for around 66% of the total water use and 7% of electricity consumption of steel production, respectively. Of the WEEN water consumption, 66% and 34% was for cooling and emission control, respectively; 96% of the WEEN electricity consumption was for emission control. The water-energy-SO2 nexus was more intensive in the coking and sintering processes, whereas the water-energy-dust nexus was more intensive in the steel-making process. Diffusion of advanced technology and improved raw material quality can reduce existing SO2 and NOx emissions by 57% and 25%, although at the expense of 5%, 56%, 0.2%, and 40% increases of dust emissions, water consumption, energy consumption, and costs, respectively. Technology sets identified by the Pareto optimization method can reduce the SO2, NOx and dust emissions by 97%, 50%, and 75%, respectively, and water and energy consumption can be reduced by 10% and 2%, respectively; however, costs could increase by 51–74%. This study highlights the need and opportunities for integrating emissions and resource use in nexus studies and production planning.

Design of super-elliptical gradient coils based on multiple objective Pareto optimization method

The design of gradient coils for a magnetic resonance imaging (MRI) system is a multiple objective optimization problem, which usually needs to deal with a couple of conflicting design objectives, such as the stored magnetic energy, power consumption, and target linear gradient distribution. These design requirements usually conflict with each other, and there is no unique optimal solution which is capable of minimizing all objectives simultaneously. Therefore, the design of gradient coils needs to be optimized reasonably with the tradeoff among different design objectives. Based on the developable property of the super-elliptical cylindrical surface and the stream function design method, the multiple objective optimization problem is analyzed by using the Pareto optimization method in this paper. The effect of proposed approach is illustrated by using the stream function method and three aforementioned coil design objectives are analyzed. The influences of the stored magnetic energy and power consumption target on linearity of gradient coil and the configuration of coils are analyzed respectively. The suitable sizes of gradient coils are discussed by analyzing the change of the stored magnetic energy. A weighted sum method is employed to produce the optimal Pareto solutions, in which the multiple objective problem reduces into a single objective function through a weighted sum of all objectives. The quantitative relationship of each design requirement is analyzed in the Pareto solution space, where Pareto optimal solutions can be intuitively found by dealing efficiently with the tradeoff among different coil properties. Numerical examples of super-elliptical gradient coil solutions are provided to demonstrate the effectiveness and versatility of the proposed method to design super-elliptical gradient coils with different coil requirements. The optimization results show that there are multiple available solutions in the convex Pareto solution space under the constraints that the linear gradient deviation is less than 5% and the magnetic stored energy and power dissipated are both no more than user-preset values. In the case that the values of summed objective functions are the same, the proposed method can intuitively see the performance of each individual target, thereby conducting to realizing the final design of gradient coils under the different design requirements. With the proposed approach, coil designers can have a reasonable overview of gradient coil design about the achievable performances of some specific properties and the competing or compatible relationships among coils properties. Therefore, a suitable design of the gradient coils for a given requirement of MRI application can be chosen reasonably.

Analysis of quality characteristics in the multicriteria synthesis problem of the production schedule

The article considers the possibility of applying the Pareto optimization method in combination with the ant algorithm for solving the task of scheduling the loading of process equipment in a flexible production system. Quality characteristics of the obtained production schedule of the machining section are analyzed depending on the choice of various combinations of optimization criteria. As a result of the experiments, carried out in the test case, it was found that with increasing number of criteria, the number of iterations required for a sufficiently accurate description of the Pareto boundary increases, which entails an increase in the algorithm running time, but the quality characteristics do not decrease significantly.

Visualization of Pareto Optimal Solution Sets Using the Growing Hierarchical Self‐Organizing Maps

SUMMARYThe visualization of the Pareto optimal solution set is one of important issues of the multiobjective 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.

Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect

This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the algorithm. In this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE 39-bus system, and obtains the Pareto optimal solutions, from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.

A Pareto Optimal Solution Visualization Method Using an Improved Growing Hierarchical Self-Organizing Maps Based on the Batch Learning

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 solutions is an important issue. This paper focuses on the Pareto optimal solution visualization method using the growing hierarchical self-organizing maps (GHSOM) which is one of promising visualization methods. This method has a superior Pareto optimal solution representation capability, compared to the visualization method using the self-organizing maps. However, this method has some shortcomings. This paper proposes a new Pareto optimal solution visualization method using an improved GHSOM based on the batch learning. In the proposed method, the batch learning algorithm is introduced to the GHSOM to obtain a consistent visualization maps for a Pareto optimal solution set. Then, the symmetric transformation of maps is introduced in the growing process in the batch learning GHSOM algorithm to improve readability of the maps. Furthermore, the learning parameter optimization is introduced. The effectiveness of the proposed method is confirmed through numerical experiments with comparing the proposed method to the conventional methods on the Pareto optimal solution representation capability and the readability of the visualization maps.