An approach to multicriterion optimization problems for engineering design

Optimization problems in mechanical engineering design are often modelled as nonlinear programming problems. A multicriterion optimization approach to this problem is developed in this work. The problem formulation is given, and the min-max principle for this problem is discussed. Next, an algorithm is provided for comparing solutions using this principle.The solution which is defined by the min-max principle of optimality may be called the best compromise considering all the criteria simultaneously and on equal terms of importance. This principle is fully formalized mathematically and used to obtain the optimal solution automatically. The algorithm for comparing solutions gives us, from any set of solutions, the one which is optimal in the min-max sense.Seeking the optimal solution in the min-max sense can be carried out in many different ways. Some methods based upon the Monte Carlo method and trade-off studies are proposed.The approach as discussed here is applied to the design of machine tool gearboxes. The problem is formulated as finding the basic constructional parameters (modules, numbers of teeth etc.) of a gearbox which minimizes simultaneously four objective functions: volume of elements, peripheral velocity between gears, width of gearbox and distance between axes of input and output shafts. A detailed example considering a lathe gearbox optimization problem is also presented. This example indicates that for some mechanical engineering optimization problems, using this approach, we can automatically obtain a solution which is optimal and acceptable to the designer.     

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.     

Modular design to support green life-cycle engineering

The severe competition in the market has driven enterprises to produce a wider variety of products to meet consumers’ needs. However, frequent variation of product specifications causes the assembly and disassembly of components and modules to become more and more complicated. As a result, the issue of product modular design is a problem worthy of concern. In this study, engineering attributes were added to the liaison graph model for the evaluation of part connections. The engineering attributes added, including contact type, combination type, tool type, and accessed direction, serve to offer designers criteria for evaluating the component liaison intensity during the design stage. A grouping genetic algorithm (GGA) is then employed for clustering the components and crossover mechanisms are modified according to the need of modular design. Furthermore, a reasonable green modular design evaluation is conducted using the green material cost analysis. According to the results, adjusted design proposals are suggested and materials that cause less pollution are recommended to replace the components with pollution values higher than those in the module. Finally, the authors use Borland C++ 6.0 to evaluate the system and clustering method. To illustrate the methodology proposed in this study, a table lamp is offered as an example.     

An effective co-evolutionary particle swarm optimization for constrained engineering design problems

Many engineering design problems can be formulated as constrained optimization problems. So far, penalty function methods have been the most popular methods for constrained optimization due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanism. By employing the notion of co-evolution to adapt penalty factors, this paper proposes a co-evolutionary particle swarm optimization approach (CPSO) for constrained optimization problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors. The proposed CPSO is population based and easy to implement in parallel. Especially, penalty factors also evolve using PSO in a self-tuning way. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method. Moreover, the CPSO obtains some solutions better than those previously reported in the literature.     

A ranking selection-based particle swarm optimizer for engineering design optimization problems

Particle swarm optimization (PSO) algorithms have been proposed to solve optimization problems in engineering design, which are usually constrained (possibly highly constrained) and may require the use of mixed variables such as continuous, integer, and discrete variables. In this paper, a new algorithm called the ranking selection-based PSO (RSPSO) is developed. In RSPSO, the objective function and constraints are handled separately. For discrete variables, they are partitioned into ordinary discrete and categorical ones, and the latter is managed and searched directly without the concept of velocity in the standard PSO. In addition, a new ranking selection scheme is incorporated into PSO to elaborately control the search behavior of a swarm in different search phases and on categorical variables. RSPSO is relatively simple and easy to implement. Experiments on five engineering problems and a benchmark function with equality constraints were conducted. The results indicate that RSPSO is an effective and widely applicable optimizer for optimization problems in engineering design in comparison with the state-of-the-art algorithms in the area.     

Multi-level parallel chaotic Jaya optimization algorithms for solving constrained engineering design problems

The Journal of Supercomputing - Several heuristic optimization algorithms have been applied to solve engineering problems. Most of these algorithms are based on populations that evolve according to...     

A novel epsilon-dominance Harris Hawks optimizer for multi-objective optimization in engineering design problems

Neural Computing and Applications - In this article, A Multi-Leaders Guided Harris Hawks optimizer using Epsilon-Dominance relation is developed for solving multi-objective optimization problems....     

Qualitative design support for engineering and architecture

Conventional design support software tools cannot effectively manage the complex, heterogeneous information used in engineering and architecture (EA) tasks. Crucially, despite uncertainty being an inherent quality of EA information particularly in the early stages of a design project, current tools solely rely on numerical approaches which do not support such incomplete and vague information. In this paper, we establish a complete framework for developing qualitative support tools that directly address these shortcomings. Our framework is application oriented and addresses the broader issues surrounding the actual use of qualitative methods. It provides design principles and strategies that allow a software engineer to develop custom qualitative software tools according to their specific EA task specifications. Our framework also provides the engineer with practical theory and guidelines for implementing their custom qualitative model and validating their system using context specific test data. We demonstrate the validity of our framework by presenting a case study in architectural lighting in which a prototype qualitative reasoning engine successfully automates qualitative logic about the subjective impressions of a lighting installation.     

The method of LP - search for the optimization of multiparametric and multicriterial problems in engineering design

Mechanical design problems can often be viewed as the search for the best system out of the set of all systems configured to perform a given function. This search, which is often subject to constraints, may be considerably aided by the use of numerical optimization procedures.A deterministic optimization procedure is described which is analogous to the well-known Monte Carlo methods and which, it is claimed, possesses improved features.     

A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties

With the increased complexity of complex engineering systems (CES), more and more disciplines, coupled relationships, work processes, design data, design knowledge and uncertainties are involved. Currently, the MDO is facing unprecedented challenges especially in dealing with the CES by different specialists dispersed geographically on heterogeneous platforms with different analysis tools. The product design data integration and data sharing among the participants and the workflow optimization hamper the development and applications of MDO in enterprises seriously. Therefore, a multi-hierarchical integrated product design data model (MiPDM) supporting the MDO in web environment and a web services-based MDO framework considering aleatory and epistemic uncertainties are proposed in this paper. With the enabling technologies including web services, ontology, workflow, agent, XML, and evidence theory, the proposed framework enables the designers geographically dispersed to work collaboratively in the MDO environment. The ontology-based workflow enables the logical reasoning of MDO to be processed dynamically. Finally, a proof-of-concept prototype system is developed based on Java 2 Platform Enterprise Edition (J2EE) and an example of supersonic business jet is demonstrated to verify the web services-based MDO framework.     

Constraint-based and SAT-based diagnosis of automotive configuration problems

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FP^NP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.     

An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.     

Approaches to control design and optimization in heat transfer problems

In this paper, boundary control problems are considered for a distributed heating system. The dynamical model of the heating system under consideration is given by a parabolic partial differential equation. In the first stage, the implementation of the Fourier method is discussed for the problem of heat convection and conduction. In the second stage, two alternative solutions to the design of tracking controllers are discussed. On the one hand, an optimal control problem is solved based on the method of integrodifferential relations. On the other hand, this procedure is used to verify the quality of a flatness-based control strategy. The results obtained by the integrodifferential approach are compared with finite-node Fourier approximations. After derivation of suitable, general-purpose solution procedures for the design of open-loop as well as closed-loop boundary control strategies, experimental results are presented. These results highlight the applicability of these procedures in a real-world experiment. For the experimental validation, a test setup at the University of Rostock has been used.     

A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

Structural and Multidisciplinary Optimization - Metamodeling is becoming a rather popular means to approximate the expensive simulations in today’s complex engineering design problems since...     

Backtracking biogeography-based optimization for numerical optimization and mechanical design problems

As a novel Evolutionary Algorithm (EA), Biogeography-Based Optimization (BBO), inspired by the science of biogeography, draws much attention due to its significant performance in both numerical simulations and practical applications. In BBO, the features in poor solutions have a large probability to be replaced by the features in good solutions. The replacement operator is termed migration. However, the replacement causes a loss of the features in poor solutions, breaks the diversity of population and may lead to a local optimal solution. To overcome this, we design a novel migration operator to propose Backtracking BBO (BBBO). In BBBO, besides the regular population, an external population is employed to record historical individuals. The size of external population is the same as the size of regular population. The external population and regular population are used together to generate the next population. After that, the individuals in external population are randomly selected to be updated by the individuals in current population. In this way, the external population in BBBO can be considered as a memory to take part in the evolutionary process. The memory takes into account both current and historical data to generate next population, which enhances algorithm’s ability in exploring searching space. In numerical simulation, 14 classical benchmarks are employed to test BBBO’s performance and several classical nature inspired algorithms are use in comparison. The results show that the strategy in BBBO is feasible and very effective to enhance algorithm’s performance. In addition, we apply BBBO to mechanical design problems which involve constraints in optimization. The comparison results also exhibit that BBBO is very competitive in solving practical optimization problems.     

Constraint-based optimization and utility elicitation using the minimax decision criterion

In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which multiple users have distinct preferences. However, making suitable decisions requires that the preferences of a specific user for different configurations be articulated or elicited, something generally acknowledged to be onerous. We address two problems associated with preference elicitation: computing a best feasible solution when the user's utilities are imprecisely specified; and developing useful elicitation procedures that reduce utility uncertainty, with minimal user interaction, to a point where (approximately) optimal decisions can be made. Our main contributions are threefold. First, we propose the use of minimax regret as a suitable decision criterion for decision making in the presence of such utility function uncertainty. Second, we devise several different procedures, all relying on mixed integer linear programs, that can be used to compute minimax regret and regret-optimizing solutions effectively. In particular, our methods exploit generalized additive structure in a user's utility function to ensure tractable computation. Third, we propose various elicitation methods that can be used to refine utility uncertainty in such a way as to quickly (i.e., with as few questions as possible) reduce minimax regret. Empirical study suggests that several of these methods are quite successful in minimizing the number of user queries, while remaining computationally practical so as to admit real-time user interaction.     

Investigating ontology development for engineering design support

Ontologies are now in widespread use as a means of formalizing domain knowledge in a way that makes it accessible, shareable and reusable. Nevertheless, to many, the nature and use of ontologies are unfamiliar. This paper takes a practical approach - through the use of example - to clarifying what ontologies are and how they might be useful in an important and representative phase of the engineering design process, that of design requirement development and capture.The paper consists of two parts. In the first part ontologies and their use are discussed, and a methodology for developing ontologies is explored. In the second part, three very different types of ontology are developed in accordance with the methodology. Each of the ontologies captures a different conceptual facet of the engineering design domain, described at a quite different level of abstraction than the others. The process of developing ontologies is illustrated in a practical way and the application of these ontologies for supporting the capture of the engineering design requirement is described as a means of demonstrating the general potential of ontologies.     

Managing the exchange of engineering product data to support through life ship design

An approach for managing the exchange of engineering product data between geographically distributed designers and analysts using a heterogeneous tool set for the through-life design of a ship is described. The approach was developed within a pan-European maritime project called VRShips-ROPAX 2000 that demonstrated how information technology could be integrated into the design process. This paper describes the development of a common model containing neutral ship product data through a bottom-up consideration of the requirements of the tools to be integrated, as well as a top-down consideration of the data requirements for through life design. This common model was supported within an Integrated Design Environment (IDE) that co-ordinated design activity distributed across Europe. The IDE ensured that the users were provided with the right data in the right form at the right time to do the right task, i.e., that the design activity was timely and appropriate. The strengths and weaknesses of the approach are highlighted.     

Automated generation of multiphysics simulation models to support multidisciplinary design optimization

To ensure a consistent design representation for serving multidisciplinary analysis, this research study proposes an intelligent modeling system to automatically generate multiphysics simulation models to support multidisciplinary design optimization processes by using a knowledge based engineering approach. A key element of this system is a multiphysics information model (MIM), which integrates the design and simulation knowledge from multiple engineering domains. The intelligent modeling system defines classes with attributes to represent various aspects of physical entities. Moreover, it uses functions to capture the non-physical information, such as control architecture, simulation test maneuvers and simulation procedures. The challenge of system coupling and the interactions among the disciplines are taken into account during the process of knowledge acquisition. Depending on the domain requirements, the intelligent modeling system extracts the required knowledge from the MIM and uses this first to instantiate submodels and second to construct the multiphysics simulation model by combining all submodels. The objective of this research is to reduce the time and effort for modeling complex systems and to provide a consistent and concurrent design environment to support multidisciplinary design optimization. The development of an unstable and unmanned aerial vehicle, a multirotor UAV, is selected as test case. The intelligent modeling system is demonstrated by modeling thirty-thousand multirotor UAV designs with different topologies and by ensuring the automatic development of a consistent control system dedicated for each individual design. Moreover, the resulting multiphysics simulation model of the multirotor UAV is validated by comparing with the flight data of an actual quadrotor UAV. The results show that the multiphysics simulation model matches test data well and indicate that high fidelity models can be generated with the automatic model generation process.