• ETRI Journal
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• v.43 no.2
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• pp.260-271
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• 2021

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N2).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.198-202
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• 2004

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a test function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.115-121
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• 2007

In this paper, we study flowshop scheduling problems with availability constraints. In such problems, n jobs have to be scheduled on m machines sequentially under assumption that the machines are unavailable during some periods of planning horizon. The objective of the problem is to find a non-permutation schedule which minimizes the makespan. As a solution procedure, we propose an improved genetic algorithm which utilizes a look-ahead schedule generator to find good solutions in a reasonable time Computational experiments show that the proposed genetic algorithm outperforms the existing genetic algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.97-97
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• 1988

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.299-304
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• 2018

An improved genetic algorithm was proposed for pattern synthesis of an adaptive beam forming system using phased array antennas. The proposed genetic algorithm is an algorithm that adds acquired characteristics procedure to solve local optimization using the diversity. The performance of the proposed genetic algorithm is verified through the problem of finding a suitable chromosome for a picture composed of binary. And it is confirmed that it is suitable for the adaptive beam forming system based on the performance problem of combining main beam and two pattern nulls.

• Korean Management Science Review
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• v.14 no.1
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• pp.77-85
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• 1997

This paper proposes an approach for individual selection in genetic algorithms to improve problem solving efficiency and effectiveness. To investigate the utility of combining simulated annealing with genetic algorithm, two experiment are conducted that compare both the conventional genetic algorithm and suggested approach. Result indicated that suggested approach significantly reduced the required time to find optimal solution in moderate-sized problems under the conditions studied. It is also found that quality of the solutions generated by suggested approach in large- sized problems is greatly improved.

• Proceedings of the KIEE Conference
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• 1999.11a
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• pp.43-51
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• 1999

The power flow calculations(PFc) are the most important and powerful tools in power systems engineering. The conventional power flow problem is solved generally with numerical methods such as Newton-Raphson(NR). The conventional numerical method generally have some convergency problem, which is sensitive to initial value, and numerical stability problem concerned with jacobian matrix inversion. This paper presents a new PFc algorithm based on the improved genetic algorithm (IGA) which can overcome the disadvantages mentioned above. The parameters of GA, with dynamical hierarchy of the coding system, are improved to make GA a practical algorithm in the problem of real system. Some case studies with test bus system also present to show the performance of proposed algorithm. The results of proposed algorithm are compared with the results of PFc obtained using a conventional NR method.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.66-76
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• 2002

Automated welding and soldering are an important manufacturing issue in order to lower the cost, increase the quality, and avoid labor problems. An off-line programming, OLP, is one of the powerful methods to solve this kind of diversity problem. Unless an OLP system is ready for the path optimization in welding and soldering, the waste of time and cost is unavoidable due to inefficient paths in welding and soldering processes. Therefore, this study attempts to obtain path optimization using a genetic algorithm based on artificial intelligences. The problem of welding path optimization is defined as a conventional TSP (traveling salesman problem), but still paths have to go through welding lines. An improved genetic algorithm was suggested and the problem was formulated as a TSP problem considering the both end points of each welding line read from database files, and then the transit problem of welding line was solved using the improved suggested genetic algorithm.

• Korean Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.215-221
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• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.