Global formulation for interactive multiobjective optimization

Interactive methods are useful and realistic multiobjective optimization techniques and, thus, many such methods exist. However, they have two important drawbacks when using them in real applications. Firstly, the question of which method should be chosen is not trivial. Secondly, there are rather few practical implementations of the methods. We introduce a general formulation that can accommodate several interactive methods. This provides a comfortable implementation framework for a general interactive system. Besides, this implementation allows the decision maker to choose how to give preference information to the system, and enables changing it anytime during the solution process. This change-of-method option provides a very flexible framework for the decision maker.     

Minimax multiobjective optimization in structural design

The use of multiobjective optimization techniques, which may be regarded as a systematic sensitivity analysis, for the selection and modification of system parameters is presented. A minimax multiobjective optimization model for structural optimization is proposed. Three typical multiobjective optimization techniques—goal programming, compromise programming and the surrogate worth trade-off method—are used to solve such a problem. The application of multiobjective optimization techniques to the selection of system parameters and large scale structural design optimization problems is the main purpose of this paper.     

Single- and multiobjective optimization problems in robust parameter design

This paper reviews the evolution of off-line quality engineering methods with respect to one or more quality criteria, and presents some recent results. The fundamental premises that justify the use of robust product/process design are established with an illustrative example. The use of designed experiments to model quality criteria and their optimization is briefly reviewed. The fact that most design-for-quality problems involve multiple quality criteria motivates the development of multiobjective optimization techniques for robust parameter design. Two situations are considered: one in which response surface models for the quality characteristics can be obtained using regression and considered over a continuous factor space, and one in which the problem scenario and the experiment permit only discrete parameter settings for the design factors. In the former scenario, a multiobjective optimization technique based on the reference-point method is presented; this technique also incorporates an inference mechanism to deal with uncertainty in the response surface models caused by finite, noisy data. In the discrete-factors scenario, an efficient method to reduce computational complexity for a class of models is presented.     

Multi-objective design optimization of a new space radiator

In this work different multi-objective techniques are used to the conceptual design of a new kind of space radiator. Called VESPAR (Variable Emittance Space Radiator), the radiator has an effective variable emittance which makes it able to reduce or avoid the demand for heater power to warm up equipment during cold case operations in orbit. The multi-objective approach was aimed on obtaining a radiator that minimize its mass while at the same time minimize the need for heater power during cold case. Four multi-objective algorithms were used: Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Simulating Annealing (MOSA) and Multi-Objective Generalized Extremal Optimization (M-GEO). The first three algorithms were used under the modeFrontier^® optimization software package, while M-GEO is a recently proposed multi-objective implementation of the Generalized Extremal Optimization (GEO) algorithm. The Pareto frontier showing the trade-off solutions between radiator mass and heater power consumption is obtained by the four algorithms and the results compared. An assessment of the performance of M-GEO on this problem, compared to the other well-known multi-objective algorithms is also made.     

An optimization design of vehicle axle system based on multiobjective cooperative optimization algorithm

ABSTRACT

To address multiobjective, multi constraint and time-consuming structural optimization problems in a vehicle axle system, a multiobjective cooperative optimization model of a vehicle axle structure is established. In light of the difficulty in the nondominated sorting of the NSGA-II algorithm caused by inconsistent effects of the uniformity objective function and physical objective function, this paper combines a multiobjective genetic algorithm with cooperative optimization and presents a strategy for handling the optimization of a vehicle axle structure. The uniformity objective function of the sub discipline is transformed to its self-constraint. Taking the multiobjective optimization of a vehicle axle system as an example, a multiobjective cooperative optimization design for the system is carried out in ISIGHT. The results show that the multiobjective cooperative optimization strategy can simplify the complexity of optimization problems and that the multiobjective cooperative optimization method based on an approximate model is favorable for accuracy and efficiency, thereby providing a theoretical basis for the optimization of similar complex structures in practical engineering.     

Efficiency for multiobjective multidisciplinary optimization problems with quasiseparable subproblems

Multidisciplinary optimization (MDO) has proved to be a useful tool for engineering design problems. Multiobjective optimization has been introduced to strengthen MDO techniques and deal with non-comparable and conflicting design objectives. A large majority of papers on multiobjective MDO have been applied in nature. This paper develops theory of multiobjective MDO and examines relationships between efficient solutions of a quasi-separable multiobjective multidisciplinary optimization problem and efficient solutions of its separable counterpart. Equivalence of the original and separable problems in the context of the Kuhn-Tucker constraint qualification and efficiency conditions are proved. Two decomposition approaches are proposed and offer a possibility of finding efficient solutions of the original problem by only finding efficient solutions of the subproblems. The presented results are related to algorithms published in the engineering literature on multiobjective MDO.     

Quadratic scalarization for decomposed multiobjective optimization

Practical applications in multidisciplinary engineering design, business management, and military planning require distributed solution approaches for solving nonconvex, multiobjective optimization problems (MOPs). Under this motivation, a quadratic scalarization method (QSM) is developed with the goal to preserve decomposable structures of the MOP while addressing nonconvexity in a manner that avoids a high degree of nonlinearity and the introduction of additional nonsmoothness. Under certain assumptions, necessary and sufficient conditions for QSM-generated solutions to be weakly and properly efficient for an MOP are developed, with any form of efficiency being understood in a local sense. QSM is shown to correspond with the relaxed, reformulated weighted-Chebyshev method as a special case. An example is provided for demonstrating the application of QSM to a nonconvex MOP.     

Co-rotational formulation for dynamic analysis of space membranes based on triangular elements

Nonlinear triangular space membrane elements are developed for the analysis of thin structures subjected to dynamic loading. By using a co-rotated framework, displacements are decomposed into rigid body motions and pure deformational displacements. The novelty of the formulation is that it employs the co-rotated framework to derive tangent dynamic matrix and an inertial force vector. Closed forms for the inertia force vector, the tangent dynamic matrix, the mass matrix and the gyroscopic matrix are derived directly from the current coordinate transformation matrix. Three numerical examples are presented to illustrate the robustness and efficiency of the new co-rotational formulation. The efficiency of the proposed approach is compared to the updated Lagrangian method, and savings in computation of up to 50 %, were achieved.     

Combustion engine optimization: a multiobjective approach

To simulate the physical and chemical processes inside combustion engines is possible by appropriate software and high performance computers. For combustion engines a good design is such that it combines a low fuel consumption with low emissions of soot and nitrogen oxides. These are however partly conflicting requirements. In this paper we approach this problem in a multi-criteria setting which has the advantage that it is possible to estimate the trade off between the different objectives and the decision of the optimal solution is postponed until all possibilities and limitations are known. The optimization algorithm is based on surrogate models and is here applied to optimize the design of a diesel combustion engine.     

Pareto navigator for interactive nonlinear multiobjective optimization

We describe a new interactive learning-oriented method called Pareto navigator for nonlinear multiobjective optimization. In the method, first a polyhedral approximation of the Pareto optimal set is formed in the objective function space using a relatively small set of Pareto optimal solutions representing the Pareto optimal set. Then the decision maker can navigate around the polyhedral approximation and direct the search for promising regions where the most preferred solution could be located. In this way, the decision maker can learn about the interdependencies between the conflicting objectives and possibly adjust one’s preferences. Once an interesting region has been identified, the polyhedral approximation can be made more accurate in that region or the decision maker can ask for the closest counterpart in the actual Pareto optimal set. If desired, (s)he can continue with another interactive method from the solution obtained. Pareto navigator can be seen as a nonlinear extension of the linear Pareto race method. After the representative set of Pareto optimal solutions has been generated, Pareto navigator is computationally efficient because the computations are performed in the polyhedral approximation and for that reason function evaluations of the actual objective functions are not needed. Thus, the method is well suited especially for problems with computationally costly functions. Furthermore, thanks to the visualization technique used, the method is applicable also for problems with three or more objective functions, and in fact it is best suited for such problems. After introducing the method in more detail, we illustrate it and the underlying ideas with an example.     

Reliable design space and complete single-loop reliability-based design optimization

Reliability-based design optimization (RBDO) has been intensively studied due to its significance and its conceptual and mathematical complexity. This paper proposes a new method for RBDO on the basis of the concept of reliable design space (RDS), within which any design satisfies the reliability requirements. Therefore, a RBDO problem becomes a simple, deterministic optimization problem constrained by RDS rather than its deterministic feasible space. The RDS is found in this work by using the partial derivatives at the current design point as an approximation of the derivatives at its corresponding most probable point (MPP) on the limit state function. This work completely resolves the double loop in RBDO and turns RBDO into a simple optimization problem. Well-known problems from the literature are selected to illustrate the steps of the approach and for result comparison. Discussions will also be given on the limitation of the proposed method, which is shown to be a common limitation overlooked by the research community on RBDO.     

Heuristic search strategies for multiobjective state space search

The multiobjective search model is a framework for solving multicriteria optimization problems using heuristic search techniques, where the different dimensions of a multiobjective search problem are mapped into a vector valued cost structure and partial order search is employed to determine the set of non-inferior solutions. This new framework for solving multicriteria optimization problems has been introduced by Stewart and White, who presented a generalization of the well known algorithmA* in this model. This paper presents several results on multiobjective state space search which helps in refining the scheme proposed by them. In particular, the following results have been presented. ? The concept ofpathmax has been generalized to the multiobjective framework. It has been established that unlike in the conventional model, multidimensionalpathmax (in the multiobjective model) is useful for nonpathological tree search instances as well. We investigate the utility of an induced total order on the partial order search mechanism. The results presented are as follows:

- If an induced total order is used in the selection process, then in general it is not necessary to compute the entire set of heuristic vectors at a node.

- In memory-bounded search, a multiobjective search strategy that uses an induced total order for selection can back up a single cost vector while backtracking and yet guarantee admissibility though multiple noninferior candidate back-up vectors may be present in the space pruned while backtracking.

? In this paper we study multiobjective state space search using inadmissible heuristics. We show that if heuristics are allowed to overestimate, then no algorithm is guaranteed to find all non-inferior solutions unless it expands dominated nodes also. The paper also addresses the task of multiobjective search under memory bounds, which is important in order to make the search scheme viable for practical purposes. The paper presents a linear space multiobjective search strategy called MOR*0 and suggests several variants of the strategy which may be useful under different situations.     

Trade-off analysis approach for interactive nonlinear multiobjective optimization

When solving multiobjective optimization problems, there is typically a decision maker (DM) who is responsible for determining the most preferred Pareto optimal solution based on his preferences. To gain confidence that the decisions to be made are the right ones for the DM, it is important to understand the trade-offs related to different Pareto optimal solutions. We first propose a trade-off analysis approach that can be connected to various multiobjective optimization methods utilizing a certain type of scalarization to produce Pareto optimal solutions. With this approach, the DM can conveniently learn about local trade-offs between the conflicting objectives and judge whether they are acceptable. The approach is based on an idea where the DM is able to make small changes in the components of a selected Pareto optimal objective vector. The resulting vector is treated as a reference point which is then projected to the tangent hyperplane of the Pareto optimal set located at the Pareto optimal solution selected. The obtained approximate Pareto optimal solutions can be used to study trade-off information. The approach is especially useful when trade-off analysis must be carried out without increasing computation workload. We demonstrate the usage of the approach through an academic example problem.     

State space formulation for composite beam-columns with partial interaction

A state space formulation is established for analyzing static responses of composite members with partial shear interaction under the combined action of an arbitrary transverse load and a constant axial force. Three generalized displacements (deflection, rotation angle, and interface slip) and three generalized forces (bending moment, shear force, and axial force) are combined into a state vector, which satisfies a state equation whose solution is easily obtained using matrix theory. The interfacial normal contact stress between the two subelements of a composite member is derived in order to check the validity of the basic assumption of identical deflection (or curvature) possessed by the two subelements. We find that, when a concentrated load alone acts on clamped–clamped or clamped–free beam-columns, tensile normal contact stress does appear at certain part of the interface. The formulation is then readily extended to analyze continuous composite beam-columns and inhomogeneous composite beam-columns. In particular, a non-continuous model of slip stiffness along the interface with discrete rectangular pulses is incorporated into the analysis, and the numerical results indicate a significant effect of slip stiffness ununiformity on the critical axial load as well as internal actions of the composite members.     

Computational strategy for multiobjective optimization of composite stiffened panels

This paper presents a multiobjective optimization methodology for composite stiffened panels. The purpose is to improve the performances of an existing design of stiffened composite panels in terms of both its first buckling load and ultimate collapse or failure loads. The design variables are the stacking sequences of the skin and of the stiffeners of the panel. The optimization is performed using a multiobjective evolutionary algorithm specifically developed for the design of laminated parts. The algorithm takes into account the industrial design guidelines for stacking sequence design. An original method is proposed for the initialization of the optimization that significantly accelerates the search for the Pareto front. In order to reduce the calculation time, Radial Basis Functions under Tension are used to approximate the objective functions. Special attention is paid to generalization errors around the optimum. The multiobjective optimization results in a wide set of trade-offs, offering important improvements for both considered objectives, among which the designer can make a choice.     

Non-convex multiobjective optimization under uncertainty: a descent algorithm. Application to sandwich plate design and reliability

In this paper a novel algorithm for solving multiobjective design optimization problems with non-smooth objective functions and uncertain parameters is presented. The algorithm is based on the existence of a common descent vector for each sample of the random objective functions and on an extension of the stochastic gradient algorithm. The proposed algorithm is applied to the optimal design of sandwich material. Comparisons with the genetic algorithm NSGA-II and the DMS solver are given and show that it is numerically more efficient due to the fact that it does not necessitate the objective function expectation evaluation. It can moreover be entirely parallelizable. Another simple illustration highlights its potential for solving general reliability problems, replacing each probability constraint by a new objective written in terms of an expectation. Moreover, for this last application, the proposed algorithm does not necessitate the computation of the (small) probability of failure.     

Finite dynamic element formulation for a plane triangular element

The higher order dynamic correction terms for the stiffness and inertia matrices associated with a triangular plane stress-strain finite dynamic element are developed in detail. Numerical results presented indicate that the adoption of these matrices along with a suitable quadratic matrix eigenproblem solver effects a significant economy in the free vibration solution of structures when compared with the analysis based on the usual finite element procedure. A FORTRAN IV computer program listing of the various relevant element matrices is also presented in the Appendix.     

A computationally efficient metamodeling approach for expensive multiobjective optimization

This paper explores a new metamodeling framework that may collapse the computational explosion that characterizes the modeling of complex systems under a multiobjective and/or multidisciplinary setting. Under the new framework, a pseudo response surface is constructed for each design objective for each discipline. This pseudo response surface has the unique property of being highly accurate in Pareto optimal regions, while it is intentionally allowed to be inaccurate in other regions. In short, the response surface for each design objective is accurate only where it matters. Because the pseudo response surface is allowed to be inaccurate in other regions of the design space, the computational cost of constructing it is dramatically reduced. An important distinguishing feature of the new framework is that the response surfaces for all the design objectives are constructed simultaneously in a mutually dependent fashion, in a way that identifies Pareto regions for the multiobjective problem. The new framework supports the puzzling notion that it is possible to obtain more accuracy and radically more design space exploration capability, while actually reducing the computation effort. This counterintuitive metamodeling paradigm shift holds the potential for identifying highly competitive products and systems that are well beyond today’s state of the art.     

Three-dimensional plasma field reconstruction with multiobjective optimization emission spectral tomography

A novel emission spectral tomography algorithm based on multiobjective optimization is proposed. Its reconstruction results for asymmetrical emission coefficient fields are studied with computer simulation. The results show that this algorithm provides a significant improvement in reconstruction precision and convergence over traditional algorithms and is suitable for real-time reconstruction of an emission-coefficient field with incomplete data. In an experiment of the argon-arc plasma diagnosis, we adopted this algorithm and the spectrum relative-intensity method to obtain the three-dimensional distributions of temperature, ionization coefficient, and electron (ion) and atom densities.