• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.223-233
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• 2008

In this paper we give sufficient conditions for a point to be a solution of an optimization problem in complex space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1972-1986
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• 2017

With the advent of the knowledge economy, science and technology resources have played an important role in economic competition, and their optimal allocation has been regarded as very important across the world. Thus, allocation optimization research for regional science and technology resources is significant for accelerating the reform of regional science and technology systems. Regional science and technology resource allocation optimization is modeled as a double-layer optimization model: the entire system is characterized by top-layer optimization, whereas the subsystems are characterized by bottom-layer optimization. To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm and sensitivity analysis. This novel method adopts the integrated modeling concept with a combination of the knowledge model and heuristic search model, on the basis of the heuristic search model, and simultaneously highlights the effect of the knowledge model. To compare the performance of different methods, five methods and two channels were used to address an application example. Both the optimized results and simulation time of the proposed method outperformed those of the other methods. The application of the proposed method to solve the problem of entire system optimization is feasible, correct, and effective.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.460-467
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• 2010

In this study, the parameters of the feedwater control system (FWCS) of the OPR1000 type nuclear power plant (NPP) are optimized by response surface methodology (RSM) in order to acquire better level control performance from the FWCS. The objective of the optimization is to minimize the steam generator (SG) water level deviation from the reference level during transients. The objective functions for this optimization are relationships between the SG level deviation and the parameters of the FWCS. However, in this case of FWCS parameter optimization, the objective functions are not available in the form of analytic equations and the responses (the SG level at plant transients) to inputs (FWCS parameters) can be evaluated by computer simulations only. Classical optimization methods cannot be used because the objective function value cannot be calculated directely. Therefore, the simulation optimization methodology is used and the RSM is adopted as the simulation optimization algorithm. Objective functions are evaluated with several typical transients in NPPs using a system simulation computer code that has been utilized for the system performance analysis of actual NPPs. The results show that the optimized parameters have better SG level control performance. The degree of the SG level deviation from the reference level during transients is minimized and consequently the control performance of the FWCS is remarkably improved.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.629-632
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• 1986

Both combinatorial and parametric optimization are used in computer-aided design(CAD). The most commonly occuring parametric optimization problems in engineering design such as design of control systems, design of electric circuits are usually expressed either as differentiable or as nondifferentiable semi-infinite programming(SIP) problems. In this paper we express algorithms for a class of computer-aided design problems arising in control systems.

• Annual Conference of KIPS
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• 2004.05a
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• pp.1453-1456
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• 2004

PARO, a power-aware routing optimization mechanism, is proposed in [1] to minimize the transmission power needed to forward packets between wireless devices in ad hoc network. The mechanism works by redirecting the route to pass through one or more intermediate nodes on behalf on source-destination pairs, then reducing the end-to-end transmission power. This paper will show an extension of this model and provide an analysis of the geometrical area lying between source and destination in which the intermediate node elects to perform redirection. The duration the intermediate node stays in that area is also computed.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.165-177
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• 2005

In this paper, an adaptive trust region method based on the conic model for unconstrained optimization problems is proposed and analyzed. We establish the global and super linear convergence results of the method. Numerical tests are reported that confirm the efficiency of the new method.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.457-459
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• 2008

This paper presents the optimization of CHP (Combined heat and power) plant under deregulated market. In this case, a boiler is added as different source for heat providing, that gives flexible and efficient operation for the plant. The purpose of optimization is to maximize the profit in period of 24 hours by making unit commitment decision, called "optimal policy". In this paper, Dynamic Programming method is introduced as the effective and efficient method. Finally, an example is solved to illustrate the optimal Policy of such a CHP and boiler.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.202-207
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• 2011

Numerous methodologies have been developed in an effort to reduce cogging torque. However, most of these methodologies have side effects that limit their applications. One approach is the optimization methodology that determines an optimized design variable within confined conditions. The response surface methodology (RSM) and the genetic algorithm (GA) are powerful instruments for such optimizations and are matters of common interest. However, they have some weaknesses. Generally, the RSM cannot accurately describe an object function, whereas the GA is time consuming. The current paper describes a novel GA and RSM hybrid algorithm that overcomes these limitations. The validity of the proposed algorithm was verified by three test functions. Its application was performed on a surface-mounted permanent magnet.