• Advances in Computational Design
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• v.6 no.1
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• pp.31-42
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• 2021

This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.503-506
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• 2005

In this study, optimization design technique for control of solid-fluid coupled force (sloshing) using evolutionary method is suggested. Artificial neural networks(ANN) and genetic algorithm(GA) is employed as evolutionary optimization method. The ANN is used to analysis of the sloshing and the genetic algorithm is adopted as an optimization algorithm. In the creation of ANN learning data, the design of experiments is adopted to higher performance of the ANN learning using minimum learning data and ALE(Arbitrary Lagrangian Eulerian) numerical method is used to obtain the sloshing analysis results. The proposed optimization technique is applied to the minimization of sloshing of the water in the tank lorry with baffles under 2 second lane change.

• Korean Institute of Interior Design Journal
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• v.21 no.1
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• pp.78-85
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• 2012

The relevance between organisms and their external environment covers everything including humans, natural and artificial surroundings, regarding which academic and scientific understanding has continued. Relevant elements established by inter-dependence between humans and environment and the unity of life should be translated from the perspective of a whole, not of unit elements or reduction. That is, a space is formed by its own program and assumes sustainable relevance based on interactions between internal and external spaces, not building an independent system. The present study aims to present the feasibility of a potential mutant space formed by invisible arenas between individuals and evolutionary space formation based on an ecological paradigm Accordingly, this study suggested that evolutionary attributes as the major power source of biological changes could verify the virtual multiplicity of a new space formation, and that the potential form generation of hybrid mutant space of emergence and infinite formative capability could be supported. The suggestions made here will hopefully contribute to extending applicability of evolutionary space generation in the field of space design. To derive the potential mutant forms from biological space, a preliminary study was conducted regarding the characteristics of evolutionary form generation. For the purpose of this study, three evolutionary perspectives of reproduction, mutation (variation) and selection were taken. First, the theory of evolution was defined and characterized. Also, the relevance between the characteristics generated and hybrid mutant space was analyzed to consider relevant characteristics. The present study helped to understand that the hybrid mutant space had an evolutionary space structure based on a biological paradigm. It was also found that the mutant space structure built by mutant polymorphism assumed a systematic correlation between space and environment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1444-1451
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• 2001

The evolutionary structural optimization(ESO) method has been under continuous development since 1992. The bidirectional evolutionary structural optimization(BESO) method is made of additive and removal procedure. The BESO method is very useful to search the global optimum and to reduce the computational time. This paper presents the ranked bidirectional evolutionary structural optimization(R-BESO) method which adds elements based on a rank, and the performance indicator which can estimate a fully stressed model. The R-BESO method can obtain the optimum design using less iteration number than iteration number of the BESO.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.795-801
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• 2006

A No Spin Differential (NSD) design has been improved from evaluation of two NSD models utilizing the axiomatic approach. New design parameters of the second level are developed to satisfy the independence axiom. The design matrices are determined to decouple the relationship between design parameters and process parameters. The values of process parameters are then determined to optimize and improve the NSD design. Consequently a unique and evolutionary NSD design is achieved with the aid of the axiomatic approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.228-233
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• 2004

Nowadays, the robot with various and complex functions is required. previous algorithms, however, cannot satisfy the requirement. In order to solve these problems, we introduce the 2-Layer Fuzzy Controller, which has a small number of fuzzy rules corresponding to various inputs and outputs. Also, it controls robustly and effectively an object. The main problem in the fuzzy controller is how to design the fuzzy rule. This paper designs the optimal 2-layer fuzzy controller using the Schema Co-Evolutionary Algorithm. The schema co-evolutionary algorithm can find more rapidly and excellently than simple genetic algorithm does.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.10-19
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• 2005

The minimum spanning tree (MST) problem is one of the traditional optimization problems. Unlike the MST, the degree constrained minimum spanning tree (DCMST) of a graph cannot, in general, be found using a polynomial time algorithm. So, finding the DCMST of a graph is a well-known NP-hard problem of importance in communications network design, road network design and other network-related problems. So, it seems to be natural to use evolutionary algorithms for solving DCMST. Especially, when applying an evolutionary algorithm to spanning tree problems, a representation and search operators should be considered simultaneously. This paper introduces a new tree representation scheme and a genetic operator for solving combinatorial tree problem using evolutionary algorithms. We performed empirical comparisons with other tree representations on several test instances and could confirm that the proposed method is superior to other tree representations. Even it is superior to edge set representation which is known as the best algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.704-711
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• 1999

This paper presents a design of fuzzy power system stabilizer (FPSS) using adaptive evolutionary computation (AEC). We have proposed an adaptive evolutionary algorithm which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. FPSS shows better control performances than conventional power system stabilizer (CPSS) in three-phase fault with heavy load which is used when tuning FPSS. To show the robustness of the proposed FPSS, it is appliedto damp the low frequency oscillations caused by disturbances such as three-phase fault with normal and light load, the angle deviation of generator with normal and light load and the angle deviation of generator with heavy load. Proposed FPSS shows better robustness than CPSS.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.369-372
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• 2000

ESO(Evolutionary Structural Optimization) method is known that elements involved low stress value are removed from the previous model or that elements are added around elements involved high stress level on it and then the optimized model is obtained with required weight. Rejection ratio/addition ratio and evolutionary ratio are predefined and elements having lower/higher stress than reference stress, which average Mises stress on edge elements times rejection ratio, are deleted/added. In this study, when the plate having a cutout is subjected various in-plane load, a cutout shape is optimized using ESO method. ANSYS is used to analyse a finite element model and optimization procedure is made by APDL (ANSYS Parametric Design Language). ESO method is useful in rather than a complex structure optimization as well as a cutout shape optimization.

• Journal of Korean Association for Spatial Structures
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• v.2 no.3 s.5
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• pp.93-102
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• 2002

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples