• Structural Engineering and Mechanics
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• v.71 no.3
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• pp.283-290
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• 2019

In global competition manufacturing companies have to produce modern, new constructions from advanced materials in order to increase competitiveness. The aim of my research was to develop a new composite cellular plate structure, which can be primarily used for structural elements of road, rail, water and air transport vehicles (e.g. vehicle bodies, ship floors). The new structure is novel and innovative, because all materials of the components of the newly developed structure are composites (laminated Carbon Fiber Reinforced Plastic (CFRP) deck plates with pultruded Glass Fiber Reinforced Plastic (GFRP) stiffeners), furthermore combines the characteristics of sandwich and cellular plate structures. The material of the structure is much more advantageous than traditional steel materials, due mainly to its low density, resulting in weight savings, causing lower fuel consumption and less environmental damage. In the study the optimal construction of a given geometry of a structural element of a road truck trailer body was defined by single- and multi-objective optimization (minimal cost and weight). During the single-objective optimization the Flexible Tolerance Optimization method, while during the multi-objective optimization the Particle Swarm Optimization method were used. Seven design constraints were considered: maximum deflection of the structure, buckling of the composite plates, buckling of the stiffeners, stress in the composite plates, stress in the stiffeners, eigenfrequency of the structure, size constraint for design variables. It was confirmed that the developed structure can be used principally as structural elements of transport vehicles and unit load devices (containers) and can be applied also in building construction.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.04a
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• pp.143-148
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• 2000

In this article, we presents multi-objective design optimization of laminated composite beam using Fuzzy programming method. At first, the two design objectives are minimizing the structural weight and maximizing the buckling load respectively. Fuzzy multi-optimization problem can be formulated based on results of single optimizations. Due to different relative importance of design objectives, membership functions are constructed by adding exponential parameters for different objective's weights. Finite element analysis of composite beam for buckling behavior are carried by Natural mode method proposed by J.Argyris and computational time of analysis can be reduced. With this scheme, a designer can conveniently obtain a compromise optimal solution of a multi-objective optimization problem only by providing some exponential parameters corresponding to the importance of the objective functions.

• Clean Technology
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• v.7 no.2
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• pp.99-107
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• 2001

Optimization methodologies have been proposed of find the best environment-friendly recycling pathways of plastic materials based on life-cycle assessment (LCA) methodology. The main difficulty in conducting this optimization study is that multiple environmental burdens have to be considered simultaneously as the cost functions. Instead of generating conservative Pareto or noninferior solutions following multi-objective optimization approaches, we have proposed some practical criteria on how to combine the different environmental burdens into a single measure. The obtained single objective optimization problem can then be solved by conventional nonlinear programming techniques or, more effectively, by a tree search method based on decision flows. The latter method reduces multi-dimensional optimization problems to a set of one-dimensional problems in series. It is expected the suggested tree search approach can be applied to many LCA studies as a new promising optimization tool.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.677-689
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• 2022

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

• Advances in Computational Design
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• v.1 no.1
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• pp.87-103
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• 2016

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.111-133
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• 2019

Flow through a scaled horizontal axis marine current turbine was numerically simulated after validation and the turbine design was optimized. The computational fluid dynamics (CFD) code Ansys-CFX 16.1 for numerical modeling, an in-house blade element momentum (BEM) code for analytical modeling and an in-house surrogate-based optimization (SBO) code were used to find an optimal turbine design. The blade-pitch angle (${\theta}$) and the number of rotor blades (NR) were taken as design variables. A single objective optimization approach was utilized in the present work. The defined objective function was the turbine's power coefficient ($C_P$). A $3{\times}3$ full-factorial sampling technique was used to define the sample space. This sampling technique gave different turbine designs, which were further evaluated for the objective function by solving the Reynolds-Averaged Navier-Stokes equations (RANS). Finally, the SBO technique with search algorithm produced an optimal design. It is found that the optimal design has improved the objective function by 26.5%. This article presents the solution approach, analysis of the turbine flow field and the predictability of various surrogate based techniques.

• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.