• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• Advances in Computational Design
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• v.7 no.4
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• pp.297-319
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• 2022

Due to their natural and social revelation, also their ease and flexibility, human collective behavior and teamwork sports are inspired to introduce optimization algorithms to solve various engineering and scientific problems. Nowadays, meta-heuristic algorithms are becoming some striking methods for solving complex real-world problems. In that respect in the present study, the authors propose a novel meta-innovative algorithm based on soccer teamwork sport, suitable for optimization problems. The method may be referred to as the Soccer League Optimization-based Championship Algorithm, inspired by the Soccer league. This method consists of two main steps, including: 1. Qualifying competitions and 2. Main competitions. To evaluate the robustness of the proposed method, six different benchmark mathematical functions, and two engineering design problem was performed for optimization to assess its efficiency in achieving optimal solutions to various problems. The results show that the proposed algorithm may well explore better performance than some well-known algorithms in various aspects such as consistency through runs and a fast and steep convergence in all problems towards the global optimal fitness value.

• Journal of Korean Society of Steel Construction
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• v.14 no.6
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• pp.783-791
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• 2002

An improved formulation for multi-objective optimization was proposed. This formulation was applied to steel seismic loads. The multi-objective optimization problem was formulated with minimum structural weight, maximum strstability. The global criterion method was employed to find a rational solution closest to the ideal solution for the optimization problem using standard steel profile, To efficiently solve the optimization problem, the decomposition meth both system-level and element-level was used. In addition, various techniques including efficient reanalysis technique intermediate variables and sensitivity analysis using an automatic differentiation(AD) were incorporated. Moreover the reamong section properties fitted to the section profile used in order to link the system level and the element level. From numerical investigation, it could be stated that the proposed method will lead to the more rational design compared with one.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.117-126
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• 2016

In this study, a new meta-heuristic optimization algorithm, Vision Correction Algorithm (VCA), designed according to the optical properties of glasses was developed. The VCA is a technique applying optometry and vision correction procedure to optimization algorithm through the process of myopic/hyperopic correction-brightness adjustment-compression enforcement-astigmatism adjustment. The proposed VCA unlike the conventional meta-heuristic algorithm is an automatically adjusting global/local search rate and global search direction based on accumulated optimization results. The proposed algorithm was applied to the representative optimization problem (mathematical and engineering problem) and results of the application are compared with that of the present algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1017-1024
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• 2010

Under the Kyoto Protocol many countries have been requested to participate in emissions trading with the assigned $CO_2$ emissions. In this environment, it is inevitable to change the system and market operation in deregulated power systems, and then ensuring safety margin is becoming more important for balancing system security, economy and $CO_2$ emissions. Nowadays, available transfer capability (ATC) is a key index of the remaining capability of a transmission system for future transactions. This paper presents a novel approach to the ATC evaluation with $CO_2$ emissions using multi-objective particle swarm optimization (MOPSO) technique. This technique evolves a multi-objective version of PSO by proposing redefinition of global best and local best individuals in multi-objective optimization domain. The optimal power flow (OPF) method using MOPSO is suggested to solve multi-objective functions including fuel cost and $CO_2$ emissions simultaneously. To show its efficiency and effectiveness, the results of the proposed method is comprehensively realized by a comparison with the ATC which is not including $CO_2$ emissions for the IEEE 30-bus system, and is found to be quite promising.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.545-551
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• 2010

A new family of DC to AC converters, referred to as multilevel inverter, has received much attention from industries and researchers for its high power and voltage applications. One of the conventional techniques for implementing the switching algorithm in these inverters is optimized harmonic stepped waveform (OHSW). However, the major problem in using this technique is eliminating low order harmonics by solving the nonlinear and complex equations. In this paper, a new approach called the "bacterial foraging algorithm" (BFA) is employed. This algorithm eliminates and optimizes the harmonics in a multilevel inverter. This method has higher speed, precision, and convergence power compared with the genetic algorithm (GA), a famous evolutionary algorithm. The proposed technique can be expanded in any number of levels. The purpose of optimization is to remove some low order harmonics, as well as to ensure the fundamental harmonic retained at the desired value. As a case study, a 13-level inverter is chosen. The comparison results by MATLAB software between the two optimization methods (BFA and GA) have shown the effectiveness and superiority of BFA over GA where convergence is desired to achieve global optimum.

• Journal of Power Electronics
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• v.6 no.4
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• pp.322-329
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• 2006

In the last decade, the increasing restrictions imposed on the exhaust emissions from internal combustion engines and traffic limitations have increased the development of electrical propulsion systems for automotive applications. The goal of electrical and hybrid vehicles is the reduction of global emissions, which in turn leads to a decrease in fuel resource exploitation. This paper presents a novel approach for control of Induction Motors (IM) using the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the Proportional Integral Controller (PI-Controller). The overall system is simulated under various operating conditions. The use of PSO as an optimization algorithm makes the drive robust and insensitive to load variation with faster dynamic response and higher accuracy. The system is tested under variable operating conditions. The simulation results show a positive dynamic response with fast recovery time.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.233-243
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• 2009

This paper concerns an algorithm that combines line search and trust region step for nonlinear optimization problems. Unlike traditional trust region methods, we incorporate the Armijo line search technique into trust region method to solve the subproblem. In addition, the subproblem is solved accurately, but instead solved by inaccurate method. If a trial step is not accepted, our algorithm performs the Armijo line search from the failed point to find a suitable steplength. At each iteration, the subproblem is solved only one time. In contrast to interior methods, the optimal solution is derived by iterating from outside of the feasible region. In numerical experiment, we apply the algorithm to nonlinear portfolio optimization problems, primary numerical results are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.168-175
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• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• Transactions on Electrical and Electronic Materials
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• v.8 no.1
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• pp.1-4
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• 2007

This paper discusses two approaches for pre-polishing optimization of oxide chemical mechanical planarization (CMP) that can be used as alternatives to the commonly applied dummy structure insertion in shallow trench isolation (STI) and replacement gate (RG) technologies: reverse nitride masking (RNM) and oxide etchback (OEB). Wafers have been produced using each optimization technique and CMP tests have been performed. Dishing, erosion and global planarity have been investigated with the help of conductive atomic force microscopy (C-AFM). The results demonstrate the effectiveness of both techniques which yield excellent planarity without dummy structure related performance degradation due to capacitive coupling.