• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.113-130
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• 2022

Sophisticated automation-based electronics world, more electrical and electronic devices are being used by people from different regions across the universe. Different manufacturers and vendors develop and market a wide variety of power generation and utilization devices under different operating parameters and conditions. People use a variety of appliances which use electrical energy as power source. These appliances or gadgets utilize the generated energy in different ratios. Night time the utilization will be less when compared with day time utilization of power. In industrial areas especially mechanical industries or Heavy machinery usage regions power utilization will be a diverse at different time intervals and it vary dynamically. This always causes a fluctuation in the grid lines because of the random and intermittent use of these apparatus while the power generating apparatus is made to operate to provide a steady output. Hence it necessitates designing and developing a method to optimize the power generated and the power utilized. Lot of methodologies has been proposed in the recent years for effective optimization and economical load dispatch. One such technique based on intelligent and evolutionary based is Black Widow Optimization BWO. To enhance the optimization level BWO is hybridized. In this research BWO based optimize the load for multi area is proposed to optimize the cost function. A three type of system was compared for economic loads of 16, 40, and 120 units. In this research work, BWO is used to improve the convergence rate and is proven statistically best in comparison to other algorithms such as HSLSO, CGBABC, SFS, ISFS. Also, BWO algorithm best optimize the cost parameter so that dynamically the load and the cost can be controlled simultaneously and hence effectively the generated power is maximum utilized at different time intervals with different load capacity in different regions of utilization.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.40-43
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• 1996

To design high quality products at low cost is one of very important task for engineers Design optimization for performances can be one solution in this task. This is robust design which has been proved effectively in many field of engineering design. In this paper, the concept of robust design is introduced and combined to fuzzy optimization and nonsingleton fuzzy logic system. The optimum parameter set points were obtained by the fuzzy optimization method and nonsingleton fuzzy logic system. These methods are applied to a filter circuit, a part of the audio circuit of mobile radio transceiver. The results are compared each other.

• Journal of Magnetics
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• v.19 no.1
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• pp.78-83
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• 2014

This paper proposes an efficient reliability-based optimization method for designing a superconducting magnetic energy system in presence of uncertainty. To evaluate the probability of failure of constraints, samplingbased reliability analysis method is employed, where Monte Carlo simulation is incorporated into dynamic Kriging models. Its main feature is to drastically reduce the numbers of iterative designs and computer simulations during the optimization process without sacrificing the accuracy of reliability analysis. Through comparison with existing methods, the validity of the proposed method is examined with the TEAM Workshop Problem 22.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.3
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• pp.225-242
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• 2016

The phase-type representation is strongly connected with the positive realization in positive system. We attempt to transform phase-type representation into sparse mono-cyclic positive representation with as low order as possible. Because equivalent positive representations of a given phase-type distribution are non-unique, it is important to find a simple sparse positive representation with lower order that leads to more effective use in applications. A Hypo-Feedback-Coxian Block (HFCB) representation is a good candidate for a simple sparse representation. Our objective is to find an HFCB representation with possibly lower order, including all the eigenvalues of the original generator. We introduce an efficient nonlinear optimization method for computing an HFCB representation from a given phase-type representation. We discuss numerical problems encountered when finding efficiently a stable solution of the nonlinear constrained optimization problem. Numerical simulations are performed to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.864-869
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• 2004

It is net easy to predict the shrinkage rate of a plastic injection mold in its design process. The shrinkage rate should be considered as one of the important performances to produce the reliable products. The shrinkage rate can be determined by suing the CAE tools in the design produces. However, since the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. In this study, the surrogate models based on the DACE is used in lien of the original models, facilitating design optimization.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.148-156
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• 1995

The past approach to burn-in optimization have been focused mainly in cost minimization problem during entire product life cycle. But in the case of practical application, burn-in optimization under reliability restriction might be preferred to burn-in optimization by cost minimization. Accordingly this paper shows real examples which estimate the practical burn-in period through application of heuristic method and select the best reliability model through comparison of reliability performance measures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.67-73
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• 1999

This paper presents resizing design optimization method by utilizing genetic algorithm(GA), which consists of three basic operators : reproduction, crossover and mutation. The fitness and penalty function for resizing optimization problem are defined, and the flowchart of the developed computer program along with the descriptions of each modules is presented. Also, modelling for flexible-body dynamic analysis is presented. The model is composed of bodies, joints, and force elements such as translational spring-damper-actuator. The design objects si to determine the wall thickness for minimum weight under dynamic displacement constraint.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.6-12
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• 2001

Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.