Multi-objective Optimization Methodology Research Articles

Efficient Multi-Objective Simulation-Driven Antenna Design Using Co-Kriging

A methodology for fast multi-objective antenna optimization is presented. Our approach is based on response surface approximation (RSA) modeling and variable-fidelity electromagnetic (EM) simulations. In the design process, a computationally cheap RSA surrogate model constructed from sampled coarse-discretization EM antenna simulations is optimized using a multi-objective evolutionary algorithm. The initially determined Pareto optimal set representing the best possible trade-offs between conflicting design objectives is then iteratively refined. In each iteration, a limited number of high-fidelity EM model responses are incorporated into the RSA model using co-kriging. The enhanced RSA model is subsequently re-optimized to yield the refined Pareto set. Combination of low- and high-fidelity simulations as well as co-kriging results in the low overall optimization cost. The proposed approach is validated using two UWB antenna examples.

Response Surface and Multiobjective Optimization Methodology for the Design of Compliant Interconnects

Compliant off-chip interconnects have both in-plane and out-of-plane compliance and are able to accommodate the differential deflection between the die and the substrate or between the substrate and the board, and thus can enhance overall reliability and life of a microelectronic system. A TriDelta compliant interconnect is being pursued at Georgia Tech as an alternate die-to-substrate or substrate-to-board interconnect. It is found that any geometry improvement that enhances the mechanical compliance will also adversely affect the electrical performance. Thus, the design of a compliant interconnect is a tradeoff between the mechanical and electrical performances. In this paper, we examine eight design variables to balance mechanical and electrical performance metrics of a TriDelta interconnect. These design variables are appropriately reduced to four and normalized. The method of normalization not only reduces the number of the design variables but also makes the response surfaces scalable with footprint size. The response surfaces are constructed for electrical resistance, inductance, and the von Mises strains using the central composite inscribed design points. The method of global criterion is used to scalarize this multiobjective optimization problem, and an optimization has been done using specified design and processing constraints as well as the ranges of the design variables. Based on the optimization results, it is seen that the TriDelta compliant interconnect geometry can be designed to meet all of the electrical, mechanical, and fabrication requirements. The developed methodology is applicable to a wide range of other compliant interconnects beyond what is presented in this paper.

Multiobjective Optimization Using Goal Programming for Industrial Water Network Design

The multiobjective optimization (MOO) of industrial water networks through goal programming is studied using a mixed-integer linear programming (MILP) formulation. First, the efficiency of goal programming for solving MOO problems is demonstrated with an introductive mathematical example and then with industrial water and energy networks design problems, formerly tackled in literature with other MOO methods. The first industrial water network case study is composed of 10 processes, 1 contaminant, and 1 water regeneration unit. The second, a more complex real industrial case study, is made of 12 processes, 1 contaminant, 4 water regeneration units, and the addition of temperature requirements for each process, which implies the introduction of energy networks alongside water networks. For MOO purposes, several antagonist objective functions are considered according to the case, such as total freshwater flow rate, number of connections, and total energy consumption. The MOO methodology proposed is demonstrated to be very reliable as an a priori method, by providing Pareto-optimal compromise solutions in significant less time compared to other traditional methods for MOO.

A satisficing approach to factor portfolio construction

Multifactor models and the construction of factor-matching portfolios are by now pervasive in investment management. In textbook discussions, the construction of factor-tracking portfolios is presented as a simple exercise solved using well-known optimization methods such as linear programming. While mathematically correct, the standard approach does not address two practical considerations. First, when building portfolios, investment managers typically consider matching the target loadings of some factors as more important than matching the target loadings of other factors. Second, investment managers generally seek to achieve their investment objectives in the most efficient way possible, by minimizing turnover and/or transaction costs. In short, portfolio construction generally requires prioritizing and reconciling multiple objectives. To address the shortcomings of the standard factor-matching framework, we show how a multi-objective optimization methodology known as ‘goal programming’ can be effectively employed to build portfolios that successfully balance competing investment aims.

MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.

The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate. In this work, we present a multi-objective heuristic optimization methodology named MACOED for detecting genetic interactions. In MACOED, we combine both logistical regression and Bayesian network methods, which are from opposing schools of statistics. The combination of these two evaluation objectives proved to be complementary, resulting in higher power with a lower false-positive rate than observed for optimizing either objective independently. To solve the space and time complexity for high-dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED that is able to retain non-dominated solutions found in past iterations. We compared MACOED with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method outperforms others in both detection power and computational feasibility for large datasets. Codes and datasets are available at: www.csbio.sjtu.edu.cn/bioinf/MACOED/.

Balancing energy efficiency and structural performance through multi-objective shape optimization: Case study of a rapidly deployable origami-inspired shelter

For military and disaster relief housing, rapidly deployable shelters must be lightweight, be packaged in a small volume for transportability, and be erected without heavy lifting equipment. A critical design criterion is also energy efficiency in heating and cooling. To meet these priorities, the research team has utilized origami as inspiration for a thermally insulated rigid wall deployable shelter that can be erected manually through counterweighting. To enhance energy efficiency, improvements in the shape of a structure (i.e., member lengths and angles) at a design stage can lead to savings throughout its lifecycle. This is magnified in the context of mass-production of deployable shelters, where any improvements are multiplied. Structural efficiency is also critical to achieve lightweight design. This paper presents a multi-objective shape optimization methodology which balances the priorities of structural performance (i.e., minimum deflections) and energy efficiency (i.e., minimum thermal energy load). This is demonstrated for the case study of a deployable shelter. Design variables include geometric parameters. Constraints relate to the package size and capability of interfacing with existing technologies. Structural analysis is performed using a parametric finite element program. Thermal energy is calculated using EnergyPlus. An optimized solution is found and compared against existing military solutions.

Multi-objective optimization based on meta-models of an aeronautical hub including the ductile damage constraint

In forging process, geometric design of initial billet and tools is very important. Traditionally, engineers use their knowledge and experience to design and optimize the geometric model of forging process by using trial-and-error methods. Such methods are time consuming and cost expensive. It is therefore interesting to design an automatic tools builder based on optimization methodology coupled with virtual finite element simulations, thus helping engineers to improve products and reduce cost. In this article we describe a meta-model based multi-objective optimization methodology for forging process designed to build the theoric Pareto optimal front of the mechanical problem. We go through a four-step process: building parametric computer-aided design geometry model, simulating the forging process according to the DOE, fitting meta-models, and optimizing the process by using an advanced algorithm. Two different meta-models, including polynomial and kriging methods, are constructed, based on the simulation values for different responses. Then optimization algorithms NBI-NLPQLP and NSGA-II are applied to find the optimum solutions based on each different meta-model. In order to drive this procedure automatically we use ModeFRONTIER® software. Using this environment, several macro commands are used to connect the geometry modelling (made with CATIA V5™) and numerical simulation process. As an industrial example, a two-step forging of an aeronautic component shows the efficiency of the proposed methodology. That shows contributions of research in dealing with optimization design of die geometry taking into account technological interactions related to the process and the ductile damage inside the deformed part. A set of solutions selected in particular points of the optimal Pareto front are also presented and analysed.

Novel steel wheel design based on multi-objective topology optimization

This paper aims to propose a multi-objective topology optimization methodology for steel wheel, in which both the compliance and eigenfrequencies are regarded as static and dynamic optimization objectives. Compromise programming method is employed to define the objectives of multi-objective and multi-stiffness topology optimizations, whereas mean-frequency formulation is adopted to settle eigenfrequencies of free vibration optimization. To obtain a clear and useful topology optimization result, cyclical symmetry and manufacturing constraints are set, the influences of which on the outcomes are also discussed. With an appropriate value of the minimum member size, a rough topology optimization of the steel wheel is obtained. The optimization result is modified according to the actual structure and manufacturing process. Moreover, based on this result, eight different steel wheel modes are established to analyze the influence of the manufacturing process and draw beads on the wheel performance through finite element simulation. Simulation results are verified by conducting a stress test of a commercially available wheel. Compared with its initial design, the optimized wheel disc exhibited decreased mass at 0.15 Kg at percentage of 4.57%, manifesting the effectiveness of the proposed method.

Optimal Water Supply System Management by Leakage Reduction and Energy Recovery

Abstract In WDS characterized by significant variation in elevation, the necessity of pumping water to higher levels is conflicted by a requirement to reduce excess pressure. A multi-objective optimization methodology is presented to minimize leakage and to minimize the difference between operational pumping costs and income generated through energy recovery by strategically locating in the network Pumps operating As Turbines (PATs), which can act in an analogous fashion to conventional PRVs. The approach is demonstrated on a case study resulting in a clear economic benefit from installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime.

A goal programming methodology for multiobjective optimization of distributed energy hubs operation

This paper addresses the problem of optimal energy flow management in multicarrier energy networks in the presence of interconnected energy hubs. The overall problem is here formalized by a nonlinear constrained multiobjective optimization problem and solved by a goal attainment based methodology. The application of this solution approach allows the analyst to identify the optimal operation state of the distributed energy hubs which ensures an effective and reliable operation of the multicarrier energy network in spite of large variations of load demands and energy prices. Simulation results obtained on the 30 bus IEEE test network are presented and discussed in order to demonstrate the significance and the validity of the proposed method.

Experimental modeling and multi-objective optimization of traveling wire electrochemical spark machining (TW-ECSM) process

The present paper attempts to focus an application of a hybrid methodology comprising of Taguchi methodology (TM) coupled with response surface methodology (RSM) for modeling and TM coupled with weighted principal component (WPC) methodology for multiobjective optimization of a self developed traveling wire electro-chemical spark machining (TW-ECSM) process. First optimum level of input parameters is found using TM which is used as the central values in RSM to develop the second-order response model. For multiobjective optimization two quality characteristics surface roughness (Ra) and material removal rate (MRR), which are of opposite nature (Ra is the lower-the-better type, while MRR is the higher-the-better type), have been selected. The WPC is employed for the calculation of weight corresponding to each quality characteristic. The results indicate that the hybrid approaches applied for modeling and optimization of the TW-ECSM process are reasonable.

Multiobjective Optimization of Experimental and Simulated Residual Stresses in Turning of Nickel-Alloy IN100

In this study, physics-based simulations are utilized to predict the forces and residual stresses induced during machining, and the results were validated using the experimental measurements. Physics-based simulations also involve uncertainty in the predicted values that can be represented as expected value and variance of the predictions. These predictions are inputted to a multiobjective optimization methodology to select the optimal machining parameters where competing or conflicting objectives constitute hurdles in the decision-making of the manufacturing plans. The objectives are chosen as related to residual stress measurements and predictions. Multiobjective particle swarm optimization (PSO) procedure is employed in optimizing process parameters. Objectives are solved for minimizing tensile residual stresses on the surface, maximizing peak compressive residual stresses, and minimizing the variance of these variables in order to increase certainty in the predictions. The optimum machining parameters corresponding to this multiobjective optimization are represented in both objective function and decision variable spaces.

Computer-aided genetic algorithm based multi-objective optimization of laser trepan drilling

The laser trepan drilling (LTD) has proven to produce better quality holes in advanced materials as compared with laser percussion drilling (LPD). But due to thermal nature of LTD process, it is rarely possible to completely remove the undesirable effects such as recast layer, heat affected zone and micro cracks. In order to improve the hole quality, these effects are required to be minimized. This research paper presents a computer-aided genetic algorithm-based multi-objective optimization (CGAMO) methodology for simultaneous optimization of multiple quality characteristics. The optimization results of the software CGAMO has been tested and validated by the published literature. Further, CGAMO has been used to simultaneously optimize the recast layer thickness (RLT) at entrance and exit in LTD of nickel based superalloy sheet. The predicted results show minimization of 99.82% and 85.06% in RLT at entrance and exit, respectively. The effect of significant process parameters on RLT has also been discussed.

A combustor liner cooling system design methodology based on a fluid/structure approach

The paper presents a multi-disciplinary multi-objective design optimization methodology of a combustion chamber effusion cooling system. The optimizer drives an Artificial Neural Network and a Manufacturing Time Model in a repeated analysis scheme in order to increase the combustor liner LCF life and to reduce the liner cooling system manufacturing time, simultaneously. The ANN is trained with a set of fluid/structure/lifing simulations arranged in a three-levels dataset based on a properly designed DOE approach. The CFD simulations are carried out with a reliable and robust in-house developed three-dimensional high resolution reactive viscous flow solver, accounting for conjugate heat transfer approach; the liner structural analysis is performed with a standard FEM code while the liner life assessments are obtained through an in-house developed software operating on the temperature/stress fields. Results demonstrate the validity of the overall approach in a five-dimensional state space with truly moderate computational costs.

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.

Liabilities – a multi-objective approach

The majority of pension plans today are underfunded, uncertain about the market, the economic and regulatory environment and searching for a more stable approach to meeting their pension benefit obligations. For plan sponsors who want to adopt a more structured approach to asset–liability management, adopting a glide path framework that gradually de-risks their investment portfolio as funded status improves has become an attractive way to move towards their funding goal. To that end, this article describes a multi-objective optimization methodology known as the goal attainment method, an approach which can accommodate the simultaneous optimization of distinct objective functions, and shows how it can be used to construct glide path strategies for asset–liability applications.

Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms

A nonlinear, multi-objective optimization methodology is presented that seeks to maximize free product recovery of light non-aqueous phase liquids (LNAPLs) while minimizing operation cost, by introducing the novel concept of optimal alternating pumping and resting periods. This process allows more oil to flow towards the extraction wells, ensuring maximum free product removal at the end of the remediation period with minimum groundwater extraction. The methodology presented here combines FEHM (Finite Element Heat and Mass transfer code), a multiphase groundwater model that simulates LNAPL transport, with three evolutionary algorithms: the genetic algorithm (GA), the differential evolution (DE) algorithm and the particle swarm optimization (PSO) algorithm. The proposed optimal free-phase recovery strategy was tested using data from a field site, located near Athens, Greece. The PSO and DE solutions were very similar, while that provided by the GA was inferior, although the computation time was roughly the same for all algorithms. One of the most efficient algorithms (PSO) was chosen to approximate the optimal Pareto front, a method that provides multiple options to decision makers. When the optimal strategy is implemented, although a significant amount of LNAPL free product is captured, a spreading of the LNAPL plume occurs. Editor Z.W. Kundzewicz; Associate editor L. See Citation Dokou, Z. and Karatzas, G.P., 2013. Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms. Hydrological Sciences Journal, 58 (3), 671–685.

Integrated waste management in batch chemical industry based on multi-objective optimization

A multi-objective optimization methodology for hazardous liquid waste management is presented in this paper using industrially based LCA models and operating constraints. This approach is used to optimize the handling of waste streams introducing flexible mixing policy scenarios compared to the rigid policy scenarios of the industrial system. It is shown that increasing the degrees of freedom for the waste mixing reduces significantly both the operating cost and the environmental impact by avoiding the use of utilities. Moreover, the influence of waste availability as function of production planning without waste storage is analyzed in several multiperiod optimizations. There, it is demonstrated that this saving potential can be further increased by integration of multiperiod production planning with waste management policies, up to the level of 40% for the environmental impact, and more than 50% for the operating cost, compared to the industrial base case. In some specific cases, a proper matching of production planning and waste mixing policies can also turn the waste treatment into a source of profit exploiting energy production from the incineration process. Implications This study reveals the savings potential of more flexible policies in waste management, in particular waste mixing of liquid waste in batch chemical industries treated in incineration, wet air oxidation, wastewater treatment plants, or recovered by distillation. Through a multi-objective optimization framework including models for operating costs and life-cycle inventories based on industrial data, operating constraints from industrial practice, and terminal constraints from legislation, savings potentials up to 50% for the operation cost and 40% for the environmental impact are demonstrated in two case studies. Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.

Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

The economic dispatch (ED) problem is one of the important optimization problems in power system operation. Recently the power system has stressed the need for reliable, nonpolluting, and economic operation. Hence, 3 conflicting functions of reliability, emission, and fuel cost are considered in the objective function of the proposed ED problem. The problem is formulated as a nonsmooth and nonconvex problem when the valve-point effects of thermal units are considered in the proposed reliable emission and economic dispatch (REED) problem. This paper presents a multiobjective optimization methodology for solving the newly developed REED problem using a fuzzified artificial bee colony algorithm. The artificial bee colony algorithm is used to schedule the optimal dispatch and fuzzy membership approach is used to find the best compromise solution from the Pareto optimal set. The methodology is validated on an IEEE 30-bus system and 3-, 6-, 10-, 26-, and 40-unit systems and the results are compared with the existing literature. The results clearly show that the proposed method is able to produce well-distributed Pareto optimal solutions when compared with other methods reported in the literature.

Improving power systems operation through multiobjective optimal location of optimal unified power flow controller

This paper presents a multiobjective optimization methodology to find the optimal location for an optimal unified power flow controller (OUPFC) device. Moreover, the installation cost function of the OUPFC device is developed. The objective functions are the total fuel cost, power losses, and system loadability with and without the minimum cost of the OUPFC installation. The \varepsilon-constraint approach is implemented for the multiobjective mathematical programming formulation. The proposed algorithm is implemented in the IEEE 30- and 118-bus test systems. The solution procedure uses nonlinear programming to optimally locate the OUPFC incorporated in an optimal power flow problem considering these objective functions and improves the operation of the power system. The optimization problem is modeled in the general algebraic modeling system software using a MINOS solver. Furthermore, the results obtained by the OUPFC are compared to those of the phase shifting transformer (PST) and unified power flow controller (UPFC) devices. The performance and applicability of the OUPFC is highlighted compared to the PST and UPFC from an analytical and technical point of view.