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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.682-685
• /
• 2014

In this paper, an improved designed of the circularly polarization antenna for sensor node. Stochastic optimization algorithms of Particle Swarm Optimization (PSO) and Adaptive Particle Swam Optimization(APSO) are studied and compared. To verify that the APSO is working better than the standard PSO, the design of a circularly polarization antenna is shows the optimized result with 27 iterations in the APSO and 41 iterations in th PSO.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.179-182
• /
• 2003

This paper proposes an adaptive hybrid genetic algorithm (aHGA) for effectively solving the complex reliability optimization problems. The proposed aHGA uses a loca1 search technique and an adaptive scheme for respectively constructing hybrid algorithm and adaptive ability. For more various comparisons with the proposed adaptive algorithm, other aHGAs with conventional adaptive schemes are considered. These aHGAs are tested and analyzed using two complex reliability optimization problems. Numerical result shows that the proposed aHGA outperforms the other competing aHGAs.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.30 no.10 s.253
• /
• pp.1249-1254
• /
• 2006

Existing adaptive observers may cause the parameter drifts due to disturbances even if state estimation errors remain small. To avoid the drift phenomena in the presence of bounded disturbances, several robust adaptive observers have been introduced addressing bounds in state and parameter estimates. However, it is not easy for these observers to manipulate the size of the bounds with the selection of the observer gain. In order to reduce estimation errors, this paper introduces the (equation omitted) gain minimization problem in the adaptive observer structure, which minimizes the (equation omitted) gain between disturbances and estimation errors. The stability condition of the adaptive observer is reformulated as a linear matrix inequality, and the observer gain is optimally chosen by solving the convex optimization problem. The estimation performance is demonstrated through a numerical example.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1780-1785
• /
• 2015

In this paper, the adaptive particle swarm optimization (APSO) algorithm is employed to design a gain-enhanced antenna with a reflector for energy harvesting. We placed the reflector below the main radiating element. Its back-radiated field is reflected and added to the forward radiated field, which could increase the antenna gain. We adopt the adaptive particle swarm optimization (APSO) algorithm, which improves the speed of convergence with a high frequency solver. The result shows that performance of the optimized design successfully satisfied the design goal of the frequency band, gain and axial ratio.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.1
• /
• pp.29-35
• /
• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.9
• /
• pp.149-157
• /
• 2004

This paper presents a new method for hi-objective optimization. Ordinary weighted sum method is easy to implement, but it has two significant drawbacks: (1) the solution distribution by the weighted sum method is not uniform, and (2) the method cannot determine any solutions that reside in non-convex regions of a Pareto front. The proposed adaptive weighted sum method does not solve a multiobjective optimization in a predetermined way, but it focuses on the regions that need more refinement by imposing additional inequality constraints. It is demonstrated that the adaptive weighted sum method produces uniformly distributed solutions and finds solutions on non-convex regions. Two numerical examples and a simple structural problem are presented to verify the performance of the proposed method.

• Proceedings of the Optical Society of Korea Conference
• /
• 1989.02a
• /
• pp.85-88
• /
• 1989

Introducing and optimizing it-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a 6x8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as "6", "8", "3", and "9". the bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.uding the adaptive optimization networks is also introduced.

• Journal of computational fluids engineering
• /
• v.1 no.1
• /
• pp.134-141
• /
• 1996

Wall interference is one of the major obstacles to increase the model size and data accuracy. There have been many treatments for wall interference including interference correction and adaptive wall test section. Recently, two-flexible-walled adaptive wall test section is concluded adequate for three-dimensional test. But proper location of target line and pressure holes are critical to its success. In this study, a new adaptive algorithm which dispenses target line and dependency of pressure hole distribution is suggested. The wind tunnel and free air tests are simulated by the numerical computation of Euler equations. The optimum wall shape is achieved by two variable optimization which is composed of two base streamlines. The wall interference is reduced well in the optimized result which is not sensitive to the base streamlines.

• Textile Coloration and Finishing
• /
• v.18 no.5 s.90
• /
• pp.88-93
• /
• 2006

A data compression system has been developed by combining adaptive wavelets and optimization technique. The adaptive wavelets were made by optimizing the coefficients of the wavelet matrix. The optimization procedure has been performed by criteria of minimizing the reconstruction error. The resulting adaptive basis outperformed such conventional basis as Daubechies-5 by 5-10%. It was also shown that the yarn density profiles could be compressed by over 95% without a significant loss of information.