• Journal of Electrical Engineering and Technology
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• 제7권2호
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

• International Journal of Fluid Machinery and Systems
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• 제4권1호
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• pp.14-24
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• 2011

In this paper, design optimization for mixed-flow pump impellers and diffusers has been studied using a commercial computational fluid dynamics (CFD) code and DOE (design of experiments). We also discussed how to improve the performance of the mixed-flow pump by designing the impeller and diffuser. Geometric design variables were defined by the vane plane development, which indicates the blade-angle distributions and length of the impeller and diffusers. The vane plane development was controlled using the blade-angle in a fixed meridional shape. First, the design optimization of the defined impeller geometric variables was achieved, and then the flow characteristics were analyzed in the point of incidence angle at the diffuser leading edge for the optimized impeller. Next, design optimizations of the defined diffuser shape variables were performed. The importance of the geometric design variables was analyzed using $2^k$ factorial designs, and the design optimization of the geometric variables was determined using the response surface method (RSM). The objective functions were defined as the total head and the total efficiency at the design flow rate. Based on the comparison of CFD results between the optimized pump and base design models, the reason for the performance improvement was discussed.

• 한국자동차공학회논문집
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• 제19권6호
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• pp.1-9
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• 2011

In this study, the specifications of the components of the vent vale were optimally determined in order to enhance the performance of the vent valve. Design objective was to minimize fuel leakage while satisfying the design constraints on the performance indices. To obtain the optimum solution based on real experiments, several design techniques available in PIAnO, a commercial PIDO tool, were used. First, an orthogonal array was used to generate training design points and then real experiments were performed to measure the experimental data at the training design points. Next, Kriging metamodels for the objective function and design constraints were generated using the experimental data. Finally, a genetic algorithm was employed to obtain the optimization results using the Kriging models. Fuel leakage of the optimized vent valve was found to be reduced by 95.8% compared to that of the initial one while satisfying all the design constraints.

• Structural Engineering and Mechanics
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• 제73권5호
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• pp.501-513
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• 2020

In the present work, the optimization of machining parameters to achieve the desired technological parameters such as surface roughness, tool radial vibration and material removal rate have been carried out using response surface methodology (RSM). The hard turning of EN19 alloy steel with coated carbide (GC3015) cutting tools was studied. The main problem faced in manufacturer of hard and high precision components is the selection of optimum combination of cutting parameters for achieving required quality of surface finish with maximum production rate. This problem can be solved by development of mathematical model and execution of experiments by RSM. A face centred central composite design (FCCD), which comes under the RSM approach, with cutting parameters (cutting speed, feed rate and depth of cut) was used for statistical analysis. A second-order regression model were developed to correlate the cutting parameters with surface roughness, tool vibration and material removal rate. Consequently, numerical and graphical optimization were performed to obtain the most appropriate cutting parameters to produce the lowest surface roughness with minimal tool vibration and maximum material removal rate using desirability function approach. Finally, confirmation experiments were performed to verify the pertinence of the developed mathematical models.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• 대한기계학회논문집A
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• 제27권6호
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• pp.864-870
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• 2003

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM(Finite Element Method) and BEM(Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta -modeling technique has been developed for solving such a complex problems combined with the DACE(Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building approximation models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty -six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging model of a train suspension. After each Kriging model is constructed, multi -objective optimal solutions are achieved by using a nonlinear programming method called SQP(Sequential Quadratic Programming).

• Nonlinear Functional Analysis and Applications
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• 제26권3호
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• pp.611-628
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• 2021

In this paper, we propose a new branch-reduction-and-bound algorithm to solve the nonlinear knapsack problems by using general discrete monotonic optimization techniques. The specific properties of the problem are exploited to increase the efficiency of the algorithm. Computational experiments of the algorithm on problems with up to 30 variables and 5 different constraints are reported.

• International Journal of Quality Innovation
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• 제7권3호
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• pp.46-57
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• 2006

The desirability function approach to multiple response optimization is a useful technique for the analysis of experiments in which several responses are optimized simultaneously. But the existing methods have some defects, and have to be modified to some extent. This paper proposes a new method to combine the individual desirabilities.

• 말소리와 음성과학
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• 제3권2호
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• pp.65-70
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• 2011

Recently, audio-visual speech recognition (AVSR) has been studied to cope with noise problems in speech recognition. In this paper we propose a novel method of deciding weighting factors for audio-visual information fusion. We adopt the particle swarm optimization (PSO) to weighting factor determination. The AVSR experiments show that PSO-based normalized confidence measures (NCM) improve the rejection performance of mis-recognized words by 33%.

• 한국건축시공학회지
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• 제12권6호
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• pp.664-673
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• 2012

Determining the layout of temporary facilities that support construction activities at a site is an important planning activity, as layout can significantly affect cost, quality of work, safety, and other aspects of the project. The construction site layout problem involves difficult combinatorial optimization. Recently, various artificial intelligence(AI)-based algorithms have been applied to solving many complex optimization problems, including neural networks(NN), genetic algorithms(GA), and swarm intelligence(SI) which relates to the collective behavior of social systems such as honey bees and birds. This study proposes a site facility layout optimization algorithm based on self-organizing maps(SOM). Computational experiments are carried out to justify the efficiency of the proposed method and compare it with particle swarm optimization(PSO). The results show that the proposed algorithm can be efficiently employed to solve the problem of site layout.