• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.442-448
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• 2014

Portfolio optimization in the presence of estimation error can be stabilized by incorporating norm-constraints; this result was shown by DeMiguel et al. (A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science, 5, 798-812, 2009), who reported empirical performance better than numerous competing approaches. We extend the idea of norm-constraints by introducing a powerful enhancement, grouped selection for portfolio optimization. Here, instead of merely penalizing norms of the assets being selected, we penalize groups, where within a group assets are treated alike, but across groups, the penalization may differ. The idea of groupwise selection is grounded in statistics, but to our knowledge, it is novel in the context of portfolio optimization. Novelty aside, the real benefits of groupwise selection are substantiated by experiments; our results show that groupwise asset selection leads to strategies with lower variance, higher Sharpe ratios, and even higher expected returns than the ordinary norm-constrained formulations.

• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.245-252
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• 2014

Engineering applications of global optimization techniques are recently abundant in the literature and it may be caused by both new methodologies arising and faster computers coming out. Many of the optimization techniques are based on natural or biological phenomena. This study put focus on enhancing the performace of Cuckoo Search (CS) among them since it has the least number of parameters to tune. The proposed enhancement can be achieved by applying surrogate-based optimization at every cycle of CS, which fortifies the exploitation capability of the original method. The enhanced algorithm has been applied several engineering design problems with constraints. The proposed method shows comparable or superior performance to the original method.

• Smart Structures and Systems
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• v.20 no.5
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• pp.607-618
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• 2017

In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.262-265
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• 2005

A shape optimization of flow guide is accomplished to minimize the wear rate of die in three-dimensional flat-die extrusion processes. In order to achieve the balanced flow and the uniformed distribution of the effective strain during the extrusion, a multi-objective optimization is implemented. During the process of optimization formulation, the flow balance and the deviation of strain is considered as constrained conditions. The proposed approach is applied to an extrusion of H section. Through the optimization, it has been confirmed that the wear rate of die can be minimized satisfying the constraint.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.12
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• pp.1156-1166
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• 2004

The shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. In constrained nonlinear optimization problems of thermal/fluid systems, three fundamental difficulties such as high computational cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are commonly confronted. Thus, a sequential approximate optimization (SAO) algorithm has been introduced because it is very hard to obtain the optimal solutions of fluid/thermal systems by means of gradient-based optimization techniques. In this study, the progressive quadratic response surface method (PQRSM) based on the trust region algorithm, which is one of sequential approximate optimization algorithms, is used for optimization and the heat sink is optimized by combining it with the computational fluid dynamics (CFD).

• Steel and Composite Structures
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• v.26 no.2
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• pp.163-170
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• 2018

The purpose of this paper is to present a new and efficient optimization algorithm called Jaya for optimum design of steel grillage structure. Constrained size optimization of this type of structure based on the LRFD-AISC is carried out with integer design variables by using cross-sectional area of W-shapes. The objective function of the problem is to find minimum weight of the grillage structure. The maximum stress ratio and the maximum displacement in the inner point of steel grillage structure are taken as the constraint for this optimization problem. To calculate the moment and shear force of the each member and calculate the joint displacement, the finite elements analysis is used. The developed computer program for the analysis and design of grillage structure and the optimization algorithm for Jaya are coded in MATLAB. The results obtained from this study are compared with the previous works for grillage structure. The results show that the Jaya algorithm presented in this study can be effectively used in the optimal design of grillage structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3638-3657
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• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.335-336
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• 2000

A psychoacoustic model based noise shaping method is proposed, where noise's presence with a host signal will not be perceptually noticeable. The derivation of imperceptible noise levels from the masking thresholds of the signal involves a deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In this paper, the problem is formulated as a constrained optimization, and it is demonstrated that the solution provides noise shaping where the noise excitation level conforms to the masking thresholds of the signal.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1212-1221
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• 1993

The shape optimization procedure applying finite element method was carried out for the specific purpose of analysis of a tractor chassis frame. Minimization of the mass as an objective function is executed under multiple constrained conditions of nodal displacements and stresses. The optimization process executions were succeeded in converging into single optimum solution. Although mass reduction and stress alleviation were attained by 40% and 26 to 24% respectively , the geometry of the shape is so complicated for fabrication that the refinement of the geometry is of necessity.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.56-63
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• 2015

The optimization is an important role in wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. In this paper, the real coded biogeography based optimization is proposed to minimize the operating cost with optimal setting of equality and inequality constraints of thermal power system. The proposed technique aims to improve the real coded searing ability, unravel the prematurity of solution and enhance the population assortment of the biogeography based optimization algorithm by using adaptive Gaussian mutation. This algorithm is demonstrated on the standard IEEE-30 bus system and the comparative results are made with existing population based methods.