• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.917-920
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• 2016

SpSF algorithm is direction-of-arrival estimation algorithm based on sparse representation of incident signlas. Cost function to be optimized for DOA estimation is multi-dimensional nonlinear function, which is hard to handle for optimization. After some manipulation, the problem can be cast into convex optimiztion problem. Convex optimization problem tuns out to be constrained optimization problem, where the parameter in the constraint has to be determined. The solution of the convex optimization problem is dependent on the specific parameter value in the constraint. In this paper, we propose a rule-of-thumb for determining the parameter value in the constraint. Based on the fact that the noise in the array elements is complex Gaussian distributed with zero mean, the average of the Frobenius norm of the matrix in the constraint can be rigorously derived. The parameter in the constrint is set to be two times the average of the Frobenius norm of the matrix in the constraint. It is shown that the SpSF algorithm actually works with the parameter value set by the method proposed in this paper.

• Applied Biological Chemistry
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• v.40 no.3
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• pp.225-231
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• 1997

Optimization theory was applied to a native and two imported soybeans. Failure stress and stress relaxation curve was measured with rheometer, and color was measured by colorimeter. The effects of each soybean upon the tofu texture were expressed through a non-linear canonical regression model and trace plot. Compared to the other imported soybeans, native soybean produced a higher strength in tofu texture, and showed the positive increase in viscoelastic properties such as instantaneous stress, equilibrium stress and relaxation time, whereas it had no effect on whiteness from reference blend, represented that native soy-bean showed the individual strength upon the selected rheological texture properties. Higher soaking ability in native soybean was selected as a new response for the optimization mixture process, and it contributed positively to the rheological properties of tofu. New soaking process control system during processing and desirability for the mathematical model should be applied for a better mixture design in varieties of soybeans.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.81-90
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• 2021

There are various optimization problems in real world and research continues to solve them. An optimization problem is the problem of finding a combination of parameters that maximizes or minimizes the objective function. Harmony search is a population-based metaheuristic algorithm for solving optimization problems and it is designed to mimic the improvisation of jazz music. Harmony search has been actively applied to optimization problems in various fields such as civil engineering, computer science, energy, medical science, and water quality engineering. Harmony search has a simple working principle and it has the advantage of finding good solutions quickly in constrained optimization problems. Especially there are various application cases showing high accuracy with a low number of iterations by improving the solution through the empirical derivative. In this paper, we explain working principle of Harmony search and classify the leading research in recent 3 years, review them according to category, and suggest future research directions. The research is divided into review by field, algorithmic analysis and theory, and application to real world problems. Application to real world problems is classified according to the purpose of optimization and whether or not they are hybridized with other metaheuristic algorithms.