• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• Structural Engineering and Mechanics
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• v.54 no.3
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• pp.453-474
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• 2015

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.780-787
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• 2017

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

• Advances in Energy Research
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• v.7 no.1
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• pp.35-52
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• 2020

The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1782-1791
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• 2017

In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.469-475
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• 2008

The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.401-408
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• 2006

An optimum design method for hybrid control systems is proposed in this study. By considering both active and passive control systems as a combined or a hybrid system, the optimization of the hybrid system can be achieved simultaneously. In the proposed approach, we consider design parameters of active control devices and the elements of the feedback gain matrix as design variables for the active control system. Required quantity of the added dampers are also treated as design variables for the passive control system. In the proposed method, the cost of both active and passive control devices, the required control efforts and dynamic responses of a target structure are selected as objective functions to be minimized. To effectively address the multi-objective optimization problem, we adopt a preference-based optimization model and apply a genetic algorithm as a numerical searching technique. As an example to verify the validity of the proposed optimization technique, a wind-excited 20-storey building with hybrid control systems is used and the results are presented.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.659-668
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• 2018

This paper proposes an optimization approach of a grid-connected photovoltaic and wind hybrid energy system including energy storage considering voltage fluctuation in the electricity grid. A techno-economic analysis is carried out in order to minimize the size of hybrid system by considering the benefit-cost. Lithium-ion battery type is used for both managing the electricity selling to the grid and reducing voltage fluctuation. A new technique is developed to limit the voltage perturbation caused by the solar irradiance and the wind speed through determining the state-of-charge of battery for every hour of a day. Improved particle swarm optimization (PSO) methods, referred to as FC-VACPSO which combines Fast Convergence Particle Swarm Optimization (FCPSO) method and Variable Acceleration Coefficient Based Particle Swarm Optimization (VACPSO) method are used to solve the optimization problem. A comparative study has been performed between standard PSO method and PSO based methods to extract the best size with the benefit cost. A sensitivity analysis has been studied for different kinds and costs of batteries, by considering variable and constant state-ofcharge of battery. The simulations, performed under Matlab environment, yield good results using the FC-VACPSO method regarding the convergence and the benefit cost of the hybrid system.

• Journal of Power Electronics
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• v.9 no.6
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• pp.960-968
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• 2009

This paper presents a design optimization process for interior permanent magnet synchronous motors (IPMSM) for hybrid electric compressors (HEC) which are applied to hybrid electrical vehicles. A hybrid electric compressor is composed of an electric motor driving section and an engine driving section which is connected to the engine by a pulley belt. A hybrid electric compressor driving motor requires half of the full driving power of a compressor. Even though an engine is not operated at the idling stop mode, the electric motor drives the air-conditioner compressor by itself so that the air conditioning system can produce its minimum cooling capacity. In this paper, the design optimization of an IPMSM for a 42 (V) applied voltage system is studied using the design of experiment (DOE) and response surface method (RSM) of 6sigma. The driving characteristics of this motor drive system are measured and analyzed by experiment.