A meta-level evolutionary strategy for many-criteria design: Application to improving tracking filters

We present a novel meta-level heuristic algorithm for multi-criteria search. It focuses on dynamically adapting the optimization criteria through the set of active objectives instead of using the evolutionary strategy (ES) parameters as other meta-level approaches do. The meta-level ES dynamically searches for the subset of objectives that achieves the best global performance. It assumes that the active subset can represent the real structure of the trade-off surface and consider all objectives at the same time as a pure multi-objective evolutionary approach (MOEA) would do.We have successfully applied this heuristic to improve the efficiency of tracking filters design, a real-world problem requiring effective and fast optimization techniques. Our approach yields competitive results and drastically reduces the computational cost. The results show an important advantage in efficiency with respect to previous conventional approaches for applying evolutionary algorithms (EA) to the same design problem. The proposed technique can be applied to real-world problems with a high number of active dependent objectives, a frequent occurrence in engineering design.     

Max-min surrogate-assisted evolutionary algorithm for robust design

Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variables or operating conditions, then it may not be appropriate to use this highly sensitive solution. In this paper, we focus on combining evolutionary algorithms with function approximation techniques for robust design. In particular, we investigate the application of robust genetic algorithms to problems with high dimensions. Subsequently, we present a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems. Empirical results are presented for synthetic test functions and aerodynamic shape design problems to demonstrate that the proposed algorithm converges to robust optimum designs on a limited computational budget.     

An integrated approach for shape and topology optimization of shell structures

In this paper an automated approach for simultaneous shape and topology optimization of shell structures is presented. Most research in the last decades considered these optimization techniques separately, seeking an initial optimal material layout and refining the shape of the solution later. The method developed in this work combines both optimization techniques, where the shape of the shell structure and material distribution are optimized simultaneously, with the aim of finding the optimum design that maximizes the stiffness of the shell. This formulation involves a variable ground structure for topology optimization, since the shape of the shell is modified in the course of the process. The method has been implemented into a computational model and the feasibility of the approach is demonstrated using several examples.     

A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.     

On simultaneous shape and material layout optimization of shell structures

This work presents a computational method for integrated shape and topology optimization of shell structures. Most research in the last decades considered both optimization techniques separately, seeking an initial optimal topology and refining the shape of the solution later. The method implemented in this work uses a combined approach, were the shape of the shell structure and material distribution are optimized simultaneously. This formulation involves a variable ground structure for topology optimization, since the shape of the shell mid-plane is modified in the course of the process. It was considered a simple type of design problem, where the optimization goal is to minimize the compliance with respect to the variables that control the shape, material fraction and orientation, subjected to a constraint on the total volume of material. The topology design problem has been formulated introducing a second rank layered microestructure, where material properties are computed by a “smear-out” procedure. The method has been implemented into a general optimization software called ODESSY, developed at the Institute of Mechanical Engineering in Aalborg. The computational model was tested in several numerical applications to illustrate and validate the approach.     

A level-set-based topology and shape optimization method for continuum structure under geometric constraints

Recent advances in level-set-based shape and topology optimization rely on free-form implicit representations to support boundary deformations and topological changes. In practice, a continuum structure is usually designed to meet parametric shape optimization, which is formulated directly in terms of meaningful geometric design variables, but usually does not support free-form boundary and topological changes. In order to solve the disadvantage of traditional step-type structural optimization, a unified optimization method which can fulfill the structural topology, shape, and sizing optimization at the same time is presented. The unified structural optimization model is described by a parameterized level set function that applies compactly supported radial basis functions (CS-RBFs) with favorable smoothness and accuracy for interpolation. The expansion coefficients of the interpolation function are treated as the design variables, which reflect the structural performance impacts of the topology, shape, and geometric constraints. Accordingly, the original topological shape optimization problem under geometric constraint is fully transformed into a simple parameter optimization problem; in other words, the optimization contains the expansion coefficients of the interpolation function in terms of limited design variables. This parameterization transforms the difficult shape and topology optimization problems with geometric constraints into a relatively straightforward parameterized problem to which many gradient-based optimization techniques can be applied. More specifically, the extended finite element method (XFEM) is adopted to improve the accuracy of boundary resolution. At last, combined with the optimality criteria method, several numerical examples are presented to demonstrate the applicability and potential of the presented method.     

A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.     

Interactive Evolutionary Multiobjective Search and Optimization of Set-Based Concepts

This paper deals with interactive concept-based multiobjective problems (IC-MOPs) and their solution by an evolutionary computation approach. The presented methodology is motivated by the need to support engineers during the conceptual design stage. IC-MOPs are based on a nontraditional concept-based approach to search and optimization. It involves conceptual solutions, which are represented by sets of particular solutions, with each concept having a one-to-many relation with the objective space. Such a set-based concept representation is most suitable for human–computer interaction. Here, a fundamental type of IC-MOPs, namely, the Pareto-directed one, is formally defined, and its solution is presented. Next, a new interactive concept-based multiobjective evolutionary algorithm is introduced, and measures to assess its resulting fronts are devised. Finally, the proposed approach and the suggested search algorithm are studied using both academic test functions and an engineering problem.      

Cost optimization of hybrid composite flywheel rotors for energy storage

A novel approach to composite flywheel rotor design is proposed. Flywheel development has been dominated by mobile applications where minimizing mass is critical. This technology is also attractive for various industrial applications. For these stationary applications, the design is considerably cost-driven. Hence, the energy-per-cost ratio was used as the objective function. Based on an analytical approach for calculating stresses in multi-rim hybrid composite rotors, the nonlinear optimization problem was solved using a multi-strategy optimization scheme that combines an evolutionary algorithm with a nonlinear interior-point method. The problem was solved for a sample rotor with varying cost ratio of the rim materials. Instead of an optimal solution per cost ratio, only four optimal designs were obtained with a sharp transition between designs at specific cost ratios. This sharp transition is explained by the intricate interplay that exists between the objective function and the nonlinear constraints imposed by the applied failure criteria.     

An interactive method for the selection of design criteria and the formulation of optimization problems in computer aided optimal design

This paper presents an interactive method for the selection of design criteria and the formulation of optimization problems within a computer aided optimization process of engineering systems. The key component of the proposed method is the formulation of an inverse optimization problem for the purpose of determining the design preferences of the engineer. These preferences are identified based on an interactive modification of a preliminary optimization result that is the solution of an initial problem statement. A formulation of the inverse optimization problem is presented, which is based on a weighted-sum multi-objective approach and leads to an explicit optimization problem that is computationally inexpensive to solve. Numerical studies on structural shape optimization problems show that the proposed method is able to identify the optimization criteria and the formulation of the optimization problem which drive the interactive user modifications.     

A systematic approach for generating velocity fields in shape optimization

Design velocity fields affect every stage of the shape optimization process. The progress of the optimization process, distortion of the finite element mesh, and final shape are sensitive to the quality of velocity fields. It is important to identify and generate effective velocity fields at the beginning of the process. This paper provides several criteria to determine the effectiveness of velocity fields. A systematic approach for generating these velocity fields using deformation fields is developed. The use of interactive procedures is shown to be indispensable for ensuring the effectiveness and quality of design velocity fields. General strategies and guidelines for generating velocity fields are given. Concepts of weight-reducing, stress-reducing, form-preserving, and smooth basis shapes are presented. Normalization of velocity fields is discussed. A method for controlling mesh distortion during the shape optimization process is given based on an explicit limit on the design change to prevent the Jacobian from vanishing. Two- and three-dimensional design problems are solved.     

Nonlinear Shape-Manifold Learning Approach: Concepts,Tools and Applications

In this paper, we present the concept of a “shape manifold” designed for reduced order representation of complex “shapes” encountered in mechanical problems, such as design optimization, springback or image correlation. The overall idea is to define the shape space within which evolves the boundary of the structure. The reduced representation is obtained by means of determining the intrinsic dimensionality of the problem, independently of the original design parameters, and by approximating a hyper surface, i.e. a shape manifold, connecting all admissible shapes represented using level set functions. Also, an optimal parameterization may be obtained for arbitrary shapes, where the parameters have to be defined a posteriori. We also developed the predictor-corrector optimization manifold walking algorithms in a reduced shape space that guarantee the admissibility of the solution with no additional constraints. We illustrate the approach on three diverse examples drawn from the field of computational and applied mechanics.     

Shape and topology optimization for closed liquid cell materials using extended multiscale finite element method

A new multiscale shape and topology optimization method is presented to design closed liquid cell materials based on the extended multiscale finite element method, which directly captures the small scale features to the large scale computation. The multiscale optimization method firstly focuses on seeking the optimum geometrical parameters and volume expansion of the fluid in the closed liquid cells in the microscale level in terms of maximizing the macroscale mechanical response of the structure. Furthermore, a new hierarchical multiscale optimization method is developed to optimize the macroscale distributions of closed liquid cells and the microscale shape of the fluid inclusion in the cells. In the macroscale level of the multiscale optimization method, the macroscale design domain is discretized by the multiscale coarse elements, while the shape of the fluid inclusions is set to be the design parameters in the microscale level. This method is firstly utilized to minimize the system compliance of the closed liquid cell structure. Moreover, due to the fact that non-uniform volume expansions of the fluid in cells can induce the elastic action, the multiscale optimization method is further extended to design biomimetic compliant actuators of the closed liquid cell materials. The multiscale optimization methods developed are implemented in the FE-package SiPESC, and the numerical examples are carried out to validate the accuracy of the methods proposed.     

Numerical method for shape optimization using T-spline based isogeometric method

Numerical methods for shape design sensitivity analysis and optimization have been developed for several decades. However, the finite-element-based shape design sensitivity analysis and optimization have experienced some bottleneck problems such as design parameterization and design remodeling during optimization. In this paper, as a remedy for these problems, an isogeometric-based shape design sensitivity analysis and optimization methods are developed incorporating with T-spline basis. In the shape design sensitivity analysis and optimization procedure using a standard finite element approach, the design boundary should be parameterized for the smooth variation of the boundary using a separate geometric modeler, such as a CAD system. Otherwise, the optimal design usually tends to fall into an undesirable irregular shape. In an isogeometric approach, the NURBS basis function that is used in representing the geometric model in the CAD system is directly used in the response analysis, and the design boundary is expressed by the same NURBS function as used in the analysis. Moreover, the smoothness of the NURBS can allow the large perturbation of the design boundary without a severe mesh distortion. Thus, the isogeometric shape design sensitivity analysis is free from remeshing during the optimization process. In addition, the use of T-spline basis instead of NURBS can reduce the number of degrees of freedom, so that the optimal solution can be obtained more efficiently while yielding the same optimum design shape.     

A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQP instances with two and three objectives.     

A level set approach for optimal design of smart energy harvesters

The proliferation of Micro-Electro-Mechanical Systems (MEMS), portable electronics and wireless sensing networks has raised the need for a new class of devices with self-powering capabilities. Vibration-based piezoelectric energy harvesters provide a very promising solution, as a result of their capability of converting mechanical energy into electrical energy through the direct piezoelectric effect. However, the identification of fast, accurate methods and rational criteria for the design of piezoelectric energy harvesting devices still poses a challenge. In this work, a level set-based topology optimization approach is proposed to synthesize mechanical energy harvesting devices for self-powered micro systems. The energy harvester design problem is reformulated as a variational problem based on the concept of topology optimization, where the optimal geometry is sought by maximizing the energy conversion efficiency of the device. To ensure computational efficiency, the shape gradient of the energy conversion efficiency is analytically derived using the material time derivative approach and the adjoint variable method. A design velocity field is then constructed using the steepest descent method, which is further integrated into level set methods. The reconciled level set (RLS) method is employed to solve multi-material shape and topology optimization problems, using the Merriman–Bence–Osher (MBO) operator. Designs with both single and multiple materials are presented, which constitute improvements with respect to existing energy harvesting designs.     

Surrogate-based aerodynamic shape optimization with the active subspace method

Surrogate-based optimization is criticized in high-dimensional cases because it cannot scale well with the input dimension. In order to overcome this issue, we adopt a snapshot active subspace method to reduce the input dimension. A smoothing operation of samples is used to reduce the demand for snapshots in the construction of active subspaces. This operation significantly reduces the computational cost on the one hand, and on the other hand, it leads to more feasible subspaces. We use a 90∼95% energy coverage criterion to define the dimension of the subspace. With this criterion, the surrogate-based airfoil optimization in the active subspace is both efficient and effective. We also validate this optimization approach in an ONERA M6 wing optimization case with 220 shape variables. Compared with original surrogate-based optimization, the new approach reduces the computational time by 70% and obtains a more practical design with a smaller drag.

     

Assessment of a parallel evolutionary optimization approach for efficient management of coastal aquifers

This study presents a parallel evolutionary optimization approach to determine optimal management strategies of large-scale coastal groundwater problems. The population loops of evolutionary algorithms (EA) are parallelized using shared memory parallelism to address the high computational demands of such applications. This methodology is applied to solve the management problems in an aquifer system in Kish Island, Iran using a three-dimensional density-dependent groundwater numerical model. EAs of continuous ant colony optimization (CACO), particle swarm optimization, and genetic algorithm are utilized to solve the optimization problems. By implementing the parallelization strategy, a speedup ratio of up to 3.53 on an 8-core processor is achieved in comparison with serial model. Based on solution quality and computational time criteria, the CACO robustness is observed in comparison to other EAs. Moreover, the optimization solution of the case study for a scenario of sea-level-rise indicates that a reduction of 20% in groundwater extraction rate is mainly due to the land-surface inundation caused by sea-level rise.     

Supporting decision making process with “ideal” software agents - What do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation.