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Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 351097, 2 pages http://dx.doi.org/10.1155/2013/351097 Editorial Multidisciplinary Design Optimization in Engineering Zhuming Bi, 1 Lihui Wang, 2 Chong Wu, 3 Guilin Yang, 4 and Dan Zhang 5 1 Department of Engineering, Indiana University-Purdue University Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne, IN 46805-149, USA 2 Department of Production Engineering, KTH Royal Institute of Technology, Brinellv¨ agen 68, 100 44 Stockholm, Sweden 3 School of Management, Harbin Institute of Technology, Harbin 150001, China 4 Mechatronics Group, Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075 5 Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street, ON, Canada L1H 7K4 Correspondence should be addressed to Zhuming Bi; [email protected] Received 20 August 2013; Accepted 20 August 2013 Copyright © 2013 Zhuming Bi et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. System complexity depends on the number of design param- eters in a system as well as the dynamic characteristics of design parameters in the time domain. Complex systems are generally featured as a large number of design param- eters, residential dynamic environments, strongly coupled system behaviors, and the needs of multiple design criteria. Modern systems are becoming more and more complex due to numerous factors such as the involvement of multidis- ciplinary behaviors, increasing functionalities and compo- nents, growing amount of information associated with system interactions, and the uncertainties and changes involved in systems. Multidisciplinary design optimization (MDO) is essential to the design and operation of a complex system. MDO takes into consideration all relevant disciplines simultaneously to find the global optimum which is superior to a solution from a sequence of local optimizations in individual disciplines. MDO has been successfully applied in the designs of many complex systems such as aircraſts, automobiles, shipbuilding, computers, and civic infrastructures. However, a simultane- ous consideration of multiple disciplines increases the com- plexity of design problems, and the products or systems in future tend to be even complicated. Continuing research and development efforts are in demand to advance the theories and methodologies of MDO. is special issue is dedicated to the recent progresses on theories, methodologies, and applications of MDO. e paper solicitation has received numerous responses from the researchers in the field, and, through a rigorous peer-review process, 13 papers have been accepted to represent the recent advancement of design theories, methodologies, and case studies related to MDO. ese papers can be roughly classified into two catalogues: solving strategies and applications. R. A. Borges et al. discussed the formulation and solu- tion of a system optimization using heuristic methods. ree bioinspired algorithms (Bees Colony algorithm, Firefly Colony algorithm, and Fish Swarm algorithm) are applied and compared to maximize the suppression bandwidth of a nonlinear damped system. MDO involves multiple design criteria, and two alternative ways to deal with the confliction of objectives in linear programming are Pareto set and Data Envelopment Analysis (DEA). M. Li provided a new repre- sentation of the Pareto set, which is generic to optimization problems with multi-design criteria. is representation was applied in Data Envelopment Analysis (DEA) models which simplified the derivation and acquisition of the properties associated with the Pareto set. J. W. Wang et al. presented a new algorithm with an integration of the Particle Swarm Optimization and predatory search strategy. e new algo- rithm was compared with others and showed its advantages in tackling the balance of exploitation and exploration in optimization. e heuristic algorithms can generally deal with any optimization problems; however, they have the limitations of inconsistency and low convergence rate; in particular, it is challenging to be applied in an optimization with expensive fitness functions. A surrogate model can be
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Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013, Article ID 351097, 2 pageshttp://dx.doi.org/10.1155/2013/351097
EditorialMultidisciplinary Design Optimization in Engineering
Zhuming Bi,1 Lihui Wang,2 Chong Wu,3 Guilin Yang,4 and Dan Zhang5
1 Department of Engineering, Indiana University-Purdue University Fort Wayne, 2101 East Coliseum Boulevard, Fort Wayne,IN 46805-149, USA
2Department of Production Engineering, KTH Royal Institute of Technology, Brinellvagen 68, 100 44 Stockholm, Sweden3 School of Management, Harbin Institute of Technology, Harbin 150001, China4Mechatronics Group, Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 6380755 Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street, ON, Canada L1H 7K4
Correspondence should be addressed to Zhuming Bi; [email protected]
Received 20 August 2013; Accepted 20 August 2013
Copyright © 2013 Zhuming Bi et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
System complexity depends on the number of design param-eters in a system as well as the dynamic characteristics ofdesign parameters in the time domain. Complex systemsare generally featured as a large number of design param-eters, residential dynamic environments, strongly coupledsystem behaviors, and the needs of multiple design criteria.Modern systems are becoming more and more complex dueto numerous factors such as the involvement of multidis-ciplinary behaviors, increasing functionalities and compo-nents, growing amount of information associatedwith systeminteractions, and the uncertainties and changes involved insystems.
Multidisciplinary design optimization (MDO) is essentialto the design and operation of a complex system. MDO takesinto consideration all relevant disciplines simultaneously tofind the global optimum which is superior to a solution froma sequence of local optimizations in individual disciplines.MDO has been successfully applied in the designs of manycomplex systems such as aircrafts, automobiles, shipbuilding,computers, and civic infrastructures. However, a simultane-ous consideration of multiple disciplines increases the com-plexity of design problems, and the products or systems infuture tend to be even complicated. Continuing research anddevelopment efforts are in demand to advance the theoriesand methodologies of MDO. This special issue is dedicatedto the recent progresses on theories, methodologies, andapplications of MDO. The paper solicitation has receivednumerous responses from the researchers in the field, and,
through a rigorous peer-review process, 13 papers have beenaccepted to represent the recent advancement of designtheories, methodologies, and case studies related to MDO.These papers can be roughly classified into two catalogues:solving strategies and applications.
R. A. Borges et al. discussed the formulation and solu-tion of a system optimization using heuristic methods.Three bioinspired algorithms (Bees Colony algorithm, FireflyColony algorithm, and Fish Swarm algorithm) are appliedand compared to maximize the suppression bandwidth ofa nonlinear damped system. MDO involves multiple designcriteria, and two alternative ways to deal with the conflictionof objectives in linear programming are Pareto set and DataEnvelopment Analysis (DEA). M. Li provided a new repre-sentation of the Pareto set, which is generic to optimizationproblems with multi-design criteria. This representation wasapplied in Data Envelopment Analysis (DEA) models whichsimplified the derivation and acquisition of the propertiesassociated with the Pareto set. J. W. Wang et al. presenteda new algorithm with an integration of the Particle SwarmOptimization and predatory search strategy. The new algo-rithm was compared with others and showed its advantagesin tackling the balance of exploitation and exploration inoptimization. The heuristic algorithms can generally dealwith any optimization problems; however, they have thelimitations of inconsistency and low convergence rate; inparticular, it is challenging to be applied in an optimizationwith expensive fitness functions. A surrogate model can be
2 Mathematical Problems in Engineering
integrated to alleviate these limitations. T. Kunakote andS. Bureerat proposed to integrate the Pareto evolutionaryalgorithm with several surrogate models. Different integra-tion scenarios were considered for eight structural designproblems. Computation and time are the criticalmeasures forthe efficiency of optimization methodologies. Time relates tothe number of iterations in the solving process. E. Devendorfand K. Lewis discussed the estimation of the number ofiterations for distributed design processes; the game theoryis integrated with the conventional discrete system theory forapproximation.
B. Li et al. developed a methodology to design stiffenedplate structures using evolutionary algorithms. X. Liang etal. investigated the effect of shock wave on a plate andassociated fluid-structure interaction and thickness effects.An analytical approach was proposed for the transient anal-ysis of a homogenous rectangular plate; the Mindlin platetheory and the Navier solution were integrated with Laplaceinversion technique. W. Huang and S. K. Oh proposedthe new fuzzy inference systems based on a multiobjec-tive opposition-based space search algorithm (MOSSA); thedeveloped systems are used for system identification toenhance the flexibility of fuzzy models. The MOSSA is aspace search algorithm with an opposition-based learningmethod; this new method employs an “opposite numbers”mechanism to accelerate the convergence in optimization.T. Yifei et al. formulated an energy model of bridge cranes,and the Finite Element Method was used for the structure-level optimization. Time dependence is an important issuein the design optimization. K. Meruva et al. tackled theformulation of optimization with a consideration of thefatigue life, and solved the design problemwith an integrationof analytical models and numerical approaches. This workhas its significance at both aspects of design and optimizationof components and large scale complex system. C. Liu etal. designed a new variable reluctance (VR) resolver forthe angle measurement. The models for the structure andoutput voltages of signal windings are formulated, and FiniteElement Analysis (FEA) was applied to predict the machineperformance. In optimizing a planetary gear system, Q.Huang et al. proposed a dynamic model, which took intoaccount time-varying mesh stiffness, excitation fluctuation,and gear backlash nonlinearities. The developed nonlin-ear differential equations were solved by variable step-sizeRunge-Kutta algorithm, and an optimization model wasdeveloped to minimize the system vibration. V. V. Kumaret al. discussed the support of the optimization involved indesign of fixtures and product lines; they presented a web-based framework to perform fixture design and assemblyline production concurrently. The framework was capableof dealing with the spatial and generational varieties, andcomputer-assisted tools were embedded to select suppliersand optimize fixtures.
We wish the collected works are helpful to the readersin the field of MDO. Due to the time constraint, we are notable to include more papers covering other important issuessuch as the formulation of complex optimization systems andthe MDO applications in large-scale systems. Readers areencouraged to submit their new contributions to this journal.
Acknowledgments
The guest editorial team is grateful to the authors of thespecial issue for their contributions and to the reviewers fortheir valuable comments on the submissions.
Zhuming BiLihui WangChong Wu
Guilin YangDan Zhang
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