• Title, Summary, Keyword: Multi-objective Optimization

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Generalized evolutionary optimum design of fiber-reinforced tire belt structure

  • Cho, J.R.;Lee, J.H.;Kim, K.W.;Lee, S.B.
    • Steel and Composite Structures
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    • v.15 no.4
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    • pp.451-466
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    • 2013
  • This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

Design of a Swing-arm Actuator using the Compliant Mechanism - Multi-objective Optimal Design Considering the Stiffness Effect (컴플라이언트 메커니즘을 이용한 스윙 암 액추에이터의 설계 - 강성 효과를 고려한 다중목적 최적화 설계 -)

  • Lee Choong-yong;Min Seungjae;Yoo Jeonghoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.2
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    • pp.128-134
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    • 2006
  • Topology optimization is an effective scheme to obtain the initial design concept: however, it is hard to apply in case of non-linear or multi-objective problems. In this study, a modified topology optimization method is proposed to generate a structure of a swing arm type actuator satisfying maximum compliance as well. as maximum stiffness using the multi-objective optimization. approach. The multi-objective function is defined to maximize the compliance in the direction of focusing of the actuator and the second eigen-frequency of the structure. The design of experiments are performed and the response surface functions are formulated to construct the multi-objective function. The weighting factors between conflicting functions are determined by the back-error propagation neural network and the solution of multi-objective function is acquired using the genetic algorithm.

Multi-objective Optimization of High Speed Railway Steel Bridges (고속철도 강교량의 다목적 최적설계)

  • 조효남;민대홍;정기영
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.263-270
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    • 2002
  • This study proposes a multi-objective optimum design method for a rational optimization of high-speed railway bridges. This multi-objective optimization is found to be effective in optimizing multi-objective problems that incorporate cost and dynamic responses such as vertical acceleration and displacement. These design factors are so important in the high-speed railway bridges. And the trade off method which is one of the most typical multi-objective optimization methods is used in this study, since the dynamic factors are formulated as objective function and also considered as constraints. And the Pareto curve can be obtained by performing the multi-objective optimization for real high-speed railway bridges. Thus, it is found that more reasonable design can be obtained when compared with those using conventional design procedure.

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An efficient multi-objective cuckoo search algorithm for design optimization

  • Kaveh, A.;Bakhshpoori, T.
    • 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.

Fuzzy multi-objective optimization of the laminated composite beam (복합재 적층 보의 퍼지 다목적 최적설계)

  • 이강희;구만회;이종호;홍영기;우호길
    • Proceedings of the Korean Society For Composite Materials Conference
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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.

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Optimization Design for Dynamic Characters of Electromagnetic Apparatus Based on Niche Sorting Multi-objective Particle Swarm Algorithm

  • Xu, Le;You, Jiaxin;Yu, Haidan;Liang, Huimin
    • Journal of Magnetics
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    • v.21 no.4
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    • pp.660-665
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    • 2016
  • The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization

  • Zhang, Wenzhu;Kwak, Kyung-Sup;Feng, Chengxiao
    • 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.

Multi-Objective Structural Optimization using Particle Swam Optimization (입자 군집 최적화기법을 이용한 다중목적함수 구조최적화)

  • Lee, Sang-Jin;Bae, Jungeun
    • Journal of the Architectural Institute of Korea
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    • v.36 no.11
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    • pp.281-288
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    • 2020
  • Particle swam optimization (PSO) technique is introduced to produce the Pareto optimal solutions in structural optimization. Both cost and deflection of beam to be minimized are considered as the objective functions and the values of stress and deflection are adopted as constraints. The weighted sum method is introduced to transform the multi-objective function problem into the single objective function problem and new weight function is introduced. The penalty function method is used to enforce design constraints during optimization process. Two numerical examples are carried out to verify the capability of PSO in structural optimization with multi-objective functions. From numerical results, the present PSO is a very effective way of finding Pareto front. Finally, we provide the present numerical results as future reference solutions using PSO.

Multi-Objective Optimization Model of Electricity Behavior Considering the Combination of Household Appliance Correlation and Comfort

  • Qu, Zhaoyang;Qu, Nan;Liu, Yaowei;Yin, Xiangai;Qu, Chong;Wang, Wanxin;Han, Jing
    • Journal of Electrical Engineering and Technology
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    • v.13 no.5
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    • pp.1821-1830
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    • 2018
  • With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

Meta-model Effects on Approximate Multi-objective Design Optimization of Vehicle Suspension Components (차량 현가 부품의 근사 다목적 설계 최적화에 대한 메타모델 영향도)

  • Song, Chang Yong;Choi, Ha-Young;Byon, Sung-Kwang
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.3
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    • pp.74-81
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    • 2019
  • Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.